LLM translation use cases inside an enterprise translation platform

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate. 
But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.
Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.
Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.
What is LLM translation?
LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent. 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets. 
In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.
LLM translation in an enterprise context
LLMs can generate translations. But they do not run your localization program.
Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.
That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems
A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.
Best results come from using LLM translation as a step in the translation process, accompanied by: 
Capability
What it does
Translation memory
Reuses approved language and reduces drift
Terminology management
Protects product terms and brand voice
QA procedures
Catches issues before content ships
Automation and governance
Ensures different content types follow the right path

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time. 
It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.
What Smartling provides
Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place.  AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure.  Users also gain access to security and quality features, such as auto fallback and hallucination mitigation. 
The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.
Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.
What is the difference between LLM translation and machine translation?
Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.
Where LLM translation tends to work best
LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.
Strengths
More natural phrasing and better tone adaptation
Flexible style for fast iteration and rewrites
Typical risks
Confident errors
Inconsistency or translation drift without guardrails, especially across repeated phrases
Where machine translation tends to work best
Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.
Strengths
Predictable output and strong accuracy for repetitive or structured text
Consistency at scale when precision and terminology control matter
[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →
Use case template
LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.
Customer support content and help centers
1. How LLMs help
LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.
2. How Smartling enables customer support
Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.
To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.
Marketing campaigns and launch messaging
1. How LLMs help
LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.
2. How Smartling enables marketing campaigns
Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.
Internal training and enablement
1. How LLMs help
LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.
2. How Smartling enables internal training
Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.
As one Marriott localization leader put it:


“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”
Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International
High-volume website and product content updates
1. How LLMs help
LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.
2. How Smartling enables website and content updates
IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.
In the case study, IHG notes: 
“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”
Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts
Regulated, legal, and brand-critical content
1. How LLMs help
LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.
2. How Smartling enforces review and QA
Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.
When LLM translation works best (and when it doesn’t)
LLMs work well for:
Customer-facing content where fluency and tone matter, inside a controlled workflow
As a step in a wider translation workflow that can be reviewed and QA’d
LLMs need guardrails for:
Legal content
Regulated industries
Brand-critical messaging
Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.
Smartling makes LLM translation usable at enterprise scale
While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.
Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.
If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?
Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

 

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate. 
But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.
Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.
Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.
What is LLM translation?
LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent. 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets. 
In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.
LLM translation in an enterprise context
LLMs can generate translations. But they do not run your localization program.
Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.
That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems
A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.
Best results come from using LLM translation as a step in the translation process, accompanied by: 
Capability
What it does
Translation memory
Reuses approved language and reduces drift
Terminology management
Protects product terms and brand voice
QA procedures
Catches issues before content ships
Automation and governance
Ensures different content types follow the right path

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time. 
It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.
What Smartling provides
Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place.  AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure.  Users also gain access to security and quality features, such as auto fallback and hallucination mitigation. 
The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.
Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.
What is the difference between LLM translation and machine translation?
Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.
Where LLM translation tends to work best
LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.
Strengths
More natural phrasing and better tone adaptation
Flexible style for fast iteration and rewrites
Typical risks
Confident errors
Inconsistency or translation drift without guardrails, especially across repeated phrases
Where machine translation tends to work best
Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.
Strengths
Predictable output and strong accuracy for repetitive or structured text
Consistency at scale when precision and terminology control matter
[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →
Use case template
LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.
Customer support content and help centers
1. How LLMs help
LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.
2. How Smartling enables customer support
Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.
To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.
Marketing campaigns and launch messaging
1. How LLMs help
LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.
2. How Smartling enables marketing campaigns
Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.
Internal training and enablement
1. How LLMs help
LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.
2. How Smartling enables internal training
Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.
As one Marriott localization leader put it:


“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”
Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International
High-volume website and product content updates
1. How LLMs help
LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.
2. How Smartling enables website and content updates
IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.
In the case study, IHG notes: 
“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”
Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts
Regulated, legal, and brand-critical content
1. How LLMs help
LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.
2. How Smartling enforces review and QA
Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.
When LLM translation works best (and when it doesn’t)
LLMs work well for:
Customer-facing content where fluency and tone matter, inside a controlled workflow
As a step in a wider translation workflow that can be reviewed and QA’d
LLMs need guardrails for:
Legal content
Regulated industries
Brand-critical messaging
Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.
Smartling makes LLM translation usable at enterprise scale
While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.
Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.
If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?
Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

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Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate. 
But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.
Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.
Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.
What is LLM translation?
LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent. 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets. 
In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.
LLM translation in an enterprise context
LLMs can generate translations. But they do not run your localization program.
Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.
That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems
A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.
Best results come from using LLM translation as a step in the translation process, accompanied by: 
Capability
What it does
Translation memory
Reuses approved language and reduces drift
Terminology management
Protects product terms and brand voice
QA procedures
Catches issues before content ships
Automation and governance
Ensures different content types follow the right path

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time. 
It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.
What Smartling provides
Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place.  AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure.  Users also gain access to security and quality features, such as auto fallback and hallucination mitigation. 
The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.
Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.
What is the difference between LLM translation and machine translation?
Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.
Where LLM translation tends to work best
LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.
Strengths
More natural phrasing and better tone adaptation
Flexible style for fast iteration and rewrites
Typical risks
Confident errors
Inconsistency or translation drift without guardrails, especially across repeated phrases
Where machine translation tends to work best
Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.
Strengths
Predictable output and strong accuracy for repetitive or structured text
Consistency at scale when precision and terminology control matter
[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →
Use case template
LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.
Customer support content and help centers
1. How LLMs help
LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.
2. How Smartling enables customer support
Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.
To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.
Marketing campaigns and launch messaging
1. How LLMs help
LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.
2. How Smartling enables marketing campaigns
Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.
Internal training and enablement
1. How LLMs help
LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.
2. How Smartling enables internal training
Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.
As one Marriott localization leader put it:


“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”
Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International
High-volume website and product content updates
1. How LLMs help
LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.
2. How Smartling enables website and content updates
IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.
In the case study, IHG notes: 
“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”
Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts
Regulated, legal, and brand-critical content
1. How LLMs help
LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.
2. How Smartling enforces review and QA
Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.
When LLM translation works best (and when it doesn’t)
LLMs work well for:
Customer-facing content where fluency and tone matter, inside a controlled workflow
As a step in a wider translation workflow that can be reviewed and QA’d
LLMs need guardrails for:
Legal content
Regulated industries
Brand-critical messaging
Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.
Smartling makes LLM translation usable at enterprise scale
While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.
Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.
If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?
Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate. 
But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.
Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.
Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.
What is LLM translation?
LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent. 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets. 
In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.
LLM translation in an enterprise context
LLMs can generate translations. But they do not run your localization program.
Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.
That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems
A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.
Best results come from using LLM translation as a step in the translation process, accompanied by: 
Capability
What it does
Translation memory
Reuses approved language and reduces drift
Terminology management
Protects product terms and brand voice
QA procedures
Catches issues before content ships
Automation and governance
Ensures different content types follow the right path

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time. 
It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.
What Smartling provides
Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place.  AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure.  Users also gain access to security and quality features, such as auto fallback and hallucination mitigation. 
The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.
Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.
What is the difference between LLM translation and machine translation?
Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.
Where LLM translation tends to work best
LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.
Strengths
More natural phrasing and better tone adaptation
Flexible style for fast iteration and rewrites
Typical risks
Confident errors
Inconsistency or translation drift without guardrails, especially across repeated phrases
Where machine translation tends to work best
Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.
Strengths
Predictable output and strong accuracy for repetitive or structured text
Consistency at scale when precision and terminology control matter
[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →
Use case template
LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.
Customer support content and help centers
1. How LLMs help
LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.
2. How Smartling enables customer support
Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.
To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.
Marketing campaigns and launch messaging
1. How LLMs help
LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.
2. How Smartling enables marketing campaigns
Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.
Internal training and enablement
1. How LLMs help
LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.
2. How Smartling enables internal training
Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.
As one Marriott localization leader put it:


“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”
Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International
High-volume website and product content updates
1. How LLMs help
LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.
2. How Smartling enables website and content updates
IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.
In the case study, IHG notes: 
“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”
Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts
Regulated, legal, and brand-critical content
1. How LLMs help
LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.
2. How Smartling enforces review and QA
Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.
When LLM translation works best (and when it doesn’t)
LLMs work well for:
Customer-facing content where fluency and tone matter, inside a controlled workflow
As a step in a wider translation workflow that can be reviewed and QA’d
LLMs need guardrails for:
Legal content
Regulated industries
Brand-critical messaging
Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.
Smartling makes LLM translation usable at enterprise scale
While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.
Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.
If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?
Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.
LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
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Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate. 
But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.
Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.
Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.
What is LLM translation?
LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent. 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets. 
In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.
LLM translation in an enterprise context
LLMs can generate translations. But they do not run your localization program.
Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.
That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems
A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.
Best results come from using LLM translation as a step in the translation process, accompanied by: 
Capability
What it does
Translation memory
Reuses approved language and reduces drift
Terminology management
Protects product terms and brand voice
QA procedures
Catches issues before content ships
Automation and governance
Ensures different content types follow the right path

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time. 
It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.
What Smartling provides
Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place.  AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure.  Users also gain access to security and quality features, such as auto fallback and hallucination mitigation. 
The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.
Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.
What is the difference between LLM translation and machine translation?
Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.
Where LLM translation tends to work best
LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.
Strengths
More natural phrasing and better tone adaptation
Flexible style for fast iteration and rewrites
Typical risks
Confident errors
Inconsistency or translation drift without guardrails, especially across repeated phrases
Where machine translation tends to work best
Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.
Strengths
Predictable output and strong accuracy for repetitive or structured text
Consistency at scale when precision and terminology control matter
[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →
Use case template
LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.
Customer support content and help centers
1. How LLMs help
LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.
2. How Smartling enables customer support
Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.
To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.
Marketing campaigns and launch messaging
1. How LLMs help
LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.
2. How Smartling enables marketing campaigns
Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.
Internal training and enablement
1. How LLMs help
LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.
2. How Smartling enables internal training
Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.
As one Marriott localization leader put it:


“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”
Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International
High-volume website and product content updates
1. How LLMs help
LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.
2. How Smartling enables website and content updates
IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.
In the case study, IHG notes: 
“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”
Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts
Regulated, legal, and brand-critical content
1. How LLMs help
LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.
2. How Smartling enforces review and QA
Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.
When LLM translation works best (and when it doesn’t)
LLMs work well for:
Customer-facing content where fluency and tone matter, inside a controlled workflow
As a step in a wider translation workflow that can be reviewed and QA’d
LLMs need guardrails for:
Legal content
Regulated industries
Brand-critical messaging
Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.
Smartling makes LLM translation usable at enterprise scale
While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.
Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.
If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?
Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

 

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

 

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

 

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

 

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

 

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

 

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

LLM translation is rapidly changing how enterprises approach localization. In enterprise localization, it’s not hard to find a model that can translate.

But keeping AI-powered translation output consistent across teams, content types, and continuous updates without turning localization into a bottleneck can be a major challenge.

Smartling is an enterprise translation platform and translation management system (TMS) designed to help teams operationalize translation at scale: automate workflows, maintain quality, and keep governance in place as content volume grows.

Smartling’s AI translation solutions are built for enterprise LLM translation, with automation across the workflow and quality steps that support reliable results without the manual lift of ad hoc, point-solution translation.

What is LLM translation?

LLM translation uses large language models (LLMs) to translate content from one language to another.

These models are trained on massive text datasets to understand and generate human-like language. Because of this, LLM translation often produces more natural phrasing and stronger tone adaptation than traditional machine translation (MT).

MT, most commonly neural machine translation, is designed specifically to translate between languages using large parallel text datasets. While output from MT models is highly accurate, it can fall short in terms of sounding fluent.

In enterprise settings, LLM translation works best when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation uses large language models (LLMs), which are AI models trained on massive amounts of text to understand and generate human-like language, to translate content from one language to another, often producing more natural phrasing and stronger tone adaptation than machine translation, which refers to automated translation systems (most commonly neural machine translation) trained specifically to translate between languages using large parallel text datasets.

In an enterprise setting, LLM translation is most valuable when used within workflows in a translation management system where linguistic assets, quality controls, and other features can help keep output consistent and on-brand at scale.

LLM translation in an enterprise context

LLMs can generate translations. But they do not run your localization program.

Today, many teams experiment with popular LLMs for translation, including models such as OpenAI’s GPT-4 and GPT-4o, Anthropic’s Claude, Google’s Gemini, and open-source models like Meta’s Llama. These large language models can generate fluent translations and adapt tone or style more naturally than traditional machine translation systems in some contexts.

However, using these models directly does not solve the operational challenges of enterprise localization. While an LLM can translate a passage of text, it does not manage translation memory, enforce terminology, connect to content systems, or coordinate workflows across teams and markets.

That’s why enterprises don’t rely on LLMs alone. They rely on translation management systems to operationalize AI translation across their entire localization program.

LLMs do not replace translation management systems

A translation management system exists for what enterprises can’t improvise: connecting translation to your tech stack, eliminating manual file handoffs, and supporting consistency as teams and markets expand.

Best results come from using LLM translation as a step in the translation process, accompanied by:

Smartling’s AI translation approach is designed to leverage language assets like translation memory, style guides, and glossary terms to support consistent translations across markets and reduce drift over time.

It also includes QA procedures to catch terminology issues, formatting errors, and other problems before content ships, plus automation and governance controls that route different content types through the right workflow and apply the right level of oversight.

What Smartling provides

Smartling’s AI Hub provides enterprises with the flexibility to access 20+ LLMs and MT engines all in one place. AI Hub users can safely swap between or test out different LLMs, like Amazon Bedrock, OpenAI, Microsoft, or Google models, without disrupting workflows or integration infrastructure. Users also gain access to security and quality features, such as auto fallback and hallucination mitigation.

The AI Hub also supports retrieval-augmented prompts that reference glossary and translation memory context at translation time to keep output on-brand at scale.

Smartling’s TMS is positioned around scaling multilingual programs with automated workflows and integrations, so it includes quality tools such as the LQA Suite and quality dashboards.

What is the difference between LLM translation and machine translation?

Both LLM translation and machine translation can be valuable. For enterprise teams, the choice is usually less about which method is “better” and more about what you need to optimize for: the size and structure of the input, the requirements for the output (precision, consistency, tone), and the level of control you need to manage risk.

Where LLM translation tends to work best

LLM translation is a strong fit when you need content to read naturally, match your tone, and feel human. It’s often used for customer-facing content, marketing and enablement, and select help content where fluency matters and some variation is acceptable.

Strengths

• More natural phrasing and better tone adaptation
• Flexible style for fast iteration and rewrites

Typical risks

• Confident errors
• Inconsistency or translation drift without guardrails, especially across repeated phrases

Where machine translation tends to work best

Machine translation (MT) is a strong fit when you have a large volume of input and you need predictable output with consistent terminology and precision of language. It’s commonly used for high-volume structured content, repeated strings, and large sets of similar pages.

Strengths

• Predictable output and strong accuracy for repetitive or structured text
• Consistency at scale when precision and terminology control matter

[SPEED BUMP]
Ready to modernize your translation strategy with AI?
Get the ebook →

Use case template

LLM translation becomes most valuable when you apply it inside a governed workflow. The use cases below keep the focus on enterprise reality: the scaling problem, what LLMs improve, and how Smartling’s platform supports quality and control.

Customer support content and help centers

1. How LLMs help

LLMs can help speed up translation of help articles, troubleshooting steps, and knowledge base updates, especially when content is updated frequently and readability matters.

2. How Smartling enables customer support

Smartling helps customer support teams translate help and support content with LLMs inside controlled workflows, so you can improve fluency and tone without losing consistency. Smartling pairs AI translation with language assets like translation memory, style guides, and glossary terms to keep terminology and brand voice consistent across every market. It also adds QA steps and governance so high-impact support content follows the right review path before it goes live.

To make this easy for support teams, Smartling connects directly to common customer support platforms so you can translate content where it already lives. For example, Smartling offers customer support integrations like Salesforce Service Cloud and Intercom, plus connectors for tools like Zendesk, ServiceNow, and CXone Expert, helping teams automate the flow of support content into translation and back again.

Marketing campaigns and launch messaging

1. How LLMs help

LLMs can help with tone adaptation for campaign copy, landing pages, and lifecycle messaging so first-pass output is closer to the intent of the source.

2. How Smartling enables marketing campaigns

Smartling positions AI Human Translation as an option for high-quality, culturally nuanced translations, and notes it’s best for content types like marketing content.

Internal training and enablement

1. How LLMs help

LLMs are well suited for enablement and training content because they can translate high volumes of frequently updated materials while preserving clarity, tone, and instructional flow. This is especially helpful when you’re iterating on decks, guides, and playbooks often and need them available across many languages without losing readability or ending up with overly literal phrasing.

2. How Smartling enables internal training

Marriott’s use of Smartling is a clear example of why platform control matters for this use case: they report expanding language coverage from seven languages to as many as 38, with turnaround moving from weeks to days, and reducing translation costs by approximately 40%.

As one Marriott localization leader put it:

“Human translation was all we knew. But as translation costs took up almost half of our project budgets, it became harder to justify expanding further, both to ourselves and our stakeholders.”

• Lynnette Glaze, Director, Associate Development Strategies + Solutions, Marriott International

High-volume website and product content updates

1. How LLMs help

LLMs can accelerate translation for high-volume updates, especially when your team’s bandwidth for reviewing completed translations is limited to higher-visibility pages.

2. How Smartling enables website and content updates

IHG describes scaling website translation across 20 languages and translating over 600 million words through Smartling’s platform. IHG also emphasizes outcomes that depend on workflow automation and ongoing updates, including real-time updates and automation that streamlined workflows.

In the case study, IHG notes:

“By enabling us to scale our translation efforts across 20 languages, we've ensured our international guests receive accurate and relevant content”

• Jake Isaac, Vice President, Guest Product, Digital & Direct Channels, IHG Hotels & Resorts

Regulated, legal, and brand-critical content

1. How LLMs help

LLMs can add value by speeding up the translation process with near-instant output, but content should still be routed through strict review and QA.

2. How Smartling enforces review and QA

Smartling positions enterprise translation quality around in-platform LQA tooling and quality dashboards (built around MQM) to evaluate and improve quality in a structured way. For higher-risk content types, Smartling also offers AI Human Translation, which adds a layer of human review to AI-powered output to ensure quality.

When LLM translation works best (and when it doesn’t)

LLMs work well for:

• Customer-facing content where fluency and tone matter, inside a controlled workflow
• As a step in a wider translation workflow that can be reviewed and QA’d

LLMs need guardrails for:

• Legal content
• Regulated industries
• Brand-critical messaging

Smartling’s AI Hub enables users to set up guardrails within the platform, including custom prompts, security and data protection, and features like auto fallback and hallucination mitigation. It also supports RAG-powered prompts that reference glossary and translation memory at translation time to keep output on brand at scale.

Smartling makes LLM translation usable at enterprise scale

While LLMs are powerful translation tools, they are just one piece of a translation workflow. Enterprises still need a platform, not point solutions.

Smartling integrates LLM translation into scalable localization workflows, combining workflow governance with the controls and quality steps needed to keep translations consistent across languages and touchpoints.

If you’re past the “LLMs are impressive” stage and trying to make AI translation work in the real world, the next question is always the same: where does AI actually fit, and what has to be in place to trust it?

Get the ebook for a practical guide to adopting AI translation in an enterprise setting, including where it performs best, what guardrails matter most, and how to roll it out without losing control of quality, terminology, or brand voice.

Tags: Language Services Cloud Translation Machine Translation