AI Translation & Machine Translation Post-Editing
AI and machine translation can accelerate multilingual workflows. Human linguists provide the context, judgment and accountability that make the output usable.
- Human post-editing on every workflow
- Content suitability assessed first
- Terminology and QA applied to machine output
What we do
Using AI where it helps, and people where it matters
Machine translation and generative AI can produce a usable first draft quickly. What they cannot do on their own is take responsibility for context, brand voice, terminology or risk. That is the job of the linguist who reviews and edits the output.
Anuvaad treats AI-assisted translation as a workflow decision, not a default. For each content type we look at how the text will be used, who will read it, how costly an error would be, and whether your languages and domain suit machine output, then recommend a fully human workflow, light post-editing or full post-editing.
The result is a process you can explain internally: what was automated, what a person checked, and to what standard.
Who uses this service
- Localization managers handling growing content volumes
- Support and knowledge base teams
- Technical documentation and product content teams
- Teams evaluating machine translation for the first time
- Teams already using MT or generative AI and needing human quality control
- Procurement teams comparing language workflows
Where it is used
- High-volume documentation and support content
- Internal or lower-risk content where speed matters
- Content refreshed frequently across many languages
- First-pass drafts that require linguistic refinement
Typical content and scope
- Technical documentation
- Knowledge base and support articles
- Product catalogues and descriptions
- Internal communication
- Training material
- Large-volume text repositories
Service capabilities
What AI Translation & MTPE covers
Each capability can be scoped on its own or combined into one program. The workflow and review depth are set per project.
- 01
Machine translation workflows
A first-pass machine translation prepared for linguist review, where the content and languages suit it.
- 02
Generative AI-assisted translation
AI-assisted drafting for suitable content, always followed by human review. Suitability is discussed before it is used.
- 03
Light post-editing
Correction of meaning errors and obvious language problems for content that needs to be understood, not polished.
- 04
Full post-editing
Editing to a publishable standard for content that will be read externally or carries brand or compliance weight.
- 05
Review of AI-generated content
Human review of text your own tools or teams have generated in one or more languages.
- 06
Terminology and glossary integration
Approved terminology applied to machine output so it matches how your organization names things.
- 07
Linguistic evaluation of MT output
Structured review of how well machine output performs on your content and language pairs.
- 08
Quality assessment and reporting
Error categories and severity recorded so you can see where machine output needs the most human effort.
- 09
Workflow suitability assessment
A recommendation on which content types suit AI assistance and which should stay fully human.
Machine translation quality varies by language pair and domain. Feasibility for your languages is confirmed during assessment, and a pilot is a sensible way to test it.
How technology and people work together
A workflow you can explain and audit
Each stage has a clear owner. Technology produces the first pass and runs consistency checks. People decide whether the content is suitable, edit the output to the agreed level and sign off on quality. The depth of human involvement scales with risk.
- Step 1
Assess suitability
Content type, audience, risk and language pair are reviewed to decide if an AI-assisted workflow fits.
Human expertise - Step 2
AI or MT first pass
A suitable engine or AI tool produces a draft, configured with your terminology where possible.
Technology-assisted - Step 3
Post-editing
Linguists edit the draft to the agreed level, light or full, correcting meaning, fluency and tone.
Human expertise - Step 4
Linguistic review
A reviewer checks the edited text against the source, with extra attention to ambiguity and sensitive content.
Human expertise - Step 5
Terminology and QA
Automated and manual checks confirm glossary use, consistency, numbers and formatting.
Quality check - Step 6
Delivery and feedback
Final content is delivered and error patterns are fed back to improve the next cycle.
Output
The workflow is shaped by
- Content type and risk
- Language pair and domain
- Terminology available
- Required quality level
- Volume and turnaround
- Data-handling requirements
Quality assurance
Quality and linguistic control for AI-assisted content
The quality bar is set before any machine output is produced, and it is checked by people rather than assumed.
Confidentiality and content handling
Client confidentiality and controlled handling of project content are considered throughout the delivery process. Specific security and data-handling requirements can be discussed during project onboarding.
Agreed post-editing level
Light or full post-editing is chosen to fit the content's purpose, and the difference is explained up front.
Human review of high-risk content
Legal, safety-related, brand-critical and culturally sensitive content receives deeper human involvement.
Terminology enforcement
Glossaries and style guidance are applied to machine output, not left to the engine.
Accuracy and fluency checks
Reviewers look for mistranslation, omissions, additions and unnatural phrasing.
Error categories and severity
Issues are classified so the pattern of machine errors is visible and can be acted on.
Sampling for larger volumes
For high-volume content, structured sampling supports consistent quality checks.
Feedback into the workflow
Recurring issues feed back into terminology, instructions and tool configuration.
Data handling agreed first
Which tools may process your content, and how, is decided during onboarding before any text is processed.
Industries and use cases
Where this work makes a difference
A few examples of how the service is applied. Every project is shaped around your content, terminology and audience.
- Technology & IT
Support and knowledge base content
High-volume, frequently updated articles where speed matters and a linguist reviews the output.
- Engineering & Manufacturing
Technical documentation refreshes
Regular updates to large documentation sets, with terminology controlled and safety-critical text kept under deeper review.
- Learning & Training
Training material at volume
Large learning libraries where a first draft accelerates the work and post-editing brings it to the right standard.
- Automotive
Catalogues and product descriptions
Repetitive, structured content where consistent terminology is applied to machine output.
- Information & Content Services
Large text repositories
Content collections that need multilingual access, with quality checks sized to how the content will be used.
- Corporate & Legal
Internal communication
Lower-risk internal content where understanding matters more than polish. Legal and policy text stays under deeper human review.
Typical project workflow
From requirement to delivery
A typical path for this service. Steps and depth are adjusted to your content, languages and risk level.
- 1
Requirement
You describe the content, languages, volume, audience and how it will be used.
- 2
Suitability assessment
We assess whether an AI-assisted workflow fits, and where a fully human workflow is better.
- 3
Linguistic strategy
We set post-editing level, terminology approach, tools and data-handling rules.
- 4
Resource assignment
Post-editors and reviewers are matched to the subject and language pair.
- 5
AI first pass and post-editing
Machine output is produced and edited to the agreed level.
- 6
Review
A reviewer checks edited content, with extra scrutiny on ambiguous or sensitive text.
- 7
Linguistic QA
Terminology, consistency and formatting are verified and error patterns recorded.
- 8
Delivery
Final content is delivered with feedback that improves the next cycle.
What you can expect to receive
- Post-edited content in your required file format
- A suitability recommendation covering which content types fit AI assistance
- Quality findings with error categories where an evaluation was in scope
- Terminology updates arising from the project
- Feedback notes to improve future machine output on your content
Ways to work with us
Pilot
A small batch to test suitability and quality on your real content before committing to a workflow.
Ongoing content programs
Regular volumes of documentation or support content with a stable post-editing process.
MT output evaluation
Linguistic review of output from tools you already use, to show where human effort is needed.
AI content review
Human review of text generated by your own systems in one or more languages.
Languages
Machine translation quality varies by language pair and domain. We confirm feasibility for your languages during project assessment.
Related services
Often combined with
Translation
For content that needs a fully human workflow.
View serviceLinguistic QA
Independent review of machine or AI-generated content.
View serviceAI & Language Services
Evaluation and annotation work for multilingual AI teams.
View serviceLocalization
When content must be adapted for a market as well as translated.
View service
FAQ
Questions buyers ask
What is MTPE?
Machine translation post-editing is a workflow in which a machine-generated translation is reviewed and edited by professional linguists to an agreed quality level.
What is the difference between light and full post-editing?
Light post-editing corrects meaning errors and obvious language problems so the text is understandable. Full post-editing brings the text to a publishable standard, with correct terminology, style and tone. We recommend the level based on how the content will be used.
When should we use AI translation?
AI-assisted workflows suit high-volume, repetitive or time-sensitive content with lower risk, such as support articles or internal documentation. We assess suitability for each content type before recommending it.
When should we not use AI translation?
Brand, legal, safety-related and culturally sensitive content usually needs deeper human involvement, and in some cases a fully human workflow is the right choice.
Will our content be sent to public AI tools?
Tool choice and data handling are discussed with you during onboarding, before any of your content is processed, so they align with your confidentiality requirements.
Can you evaluate machine translation output we already use?
Yes. Linguistic evaluation of MT output is a supported workflow. Reviewers assess accuracy and fluency on your content and language pairs and report the pattern of errors.
Can you review content generated by our own AI tools?
Yes. Human review of AI-generated multilingual content is supported, from light checks to full editing, depending on how the content will be used.
How is an AI-assisted project quoted?
Every project is assessed individually. Volume, languages, content type and the post-editing level all affect the quotation, so we do not publish fixed prices.
Not sure whether AI fits your content?
Share a sample and your requirements. We'll assess suitability and recommend the workflow, whether that is AI-assisted or fully human.