AI & Language Services
Human linguistic judgment for teams building and evaluating multilingual AI: language data, annotation and evaluation.
- Human linguistic judgment for AI teams
- Guidelines and calibration before scale
- Start with a pilot
What we do
Language expertise for teams building and evaluating multilingual AI
AI systems depend on language data and on people who can judge how those systems perform across languages. Fluency, factual accuracy, tone, safety and cultural fit are hard to measure automatically, and they are exactly where trained linguists add value.
This is a service capability area that draws on Anuvaad's linguistic experience. It covers evaluation of AI output, annotation and preparation of language data, and review of conversational, search and voice experiences in multiple languages.
Because every engagement is different, task types, tooling, language coverage and data-handling requirements are confirmed during assessment, and we recommend starting with a pilot so guidelines and quality can be calibrated before scaling.
Who uses this service
- AI and machine learning teams building multilingual products
- Product owners evaluating LLM output in several languages
- Data and annotation program managers
- Search, relevance and conversational AI teams
- Localization teams adopting generative AI
- Procurement teams sourcing language data services
Where it is used
- Evaluating LLM output across languages
- Preparing and labelling language data
- Assessing search and relevance quality
- Reviewing conversational AI and voice experiences
Typical content and scope
- LLM linguistic evaluation
- Multilingual AI evaluation
- Language data annotation
- Text annotation
- Speech data services
- Intent classification
- Entity annotation
- Sentiment and language classification
- AI-generated content review
- Search and relevance evaluation
- Multilingual conversational AI evaluation
- Speech and voice quality evaluation
Service capabilities
What AI & Language Services covers
Each capability can be scoped on its own or combined into one program. The workflow and review depth are set per project.
- 01
LLM linguistic evaluation
Human review of model output for fluency, accuracy, tone and appropriateness in the target language.
- 02
Multilingual AI evaluation
Comparison of model behavior across languages against your criteria and rubrics.
- 03
Text annotation
Labelling of text for entities, topics, categories and other attributes, following your guidelines.
- 04
Intent and entity annotation
Annotation for conversational and search systems, including intents, slots and named entities.
- 05
Sentiment and language classification
Classification of text by sentiment, language, register or other agreed dimensions.
- 06
Language data preparation
Review, cleaning and translation of language datasets for use in multilingual systems.
- 07
Speech data services
Recording, transcription and review of speech data. Scope and languages are confirmed per engagement.
- 08
Search and relevance evaluation
Human judgment of how relevant and useful search or recommendation results are in each language.
- 09
Conversational AI evaluation
Review of chatbot and assistant conversations for accuracy, tone, safety and language quality.
- 10
Speech and voice quality evaluation
Assessment of speech recognition and voice output for clarity, naturalness and accuracy.
- 11
AI-generated content review
Human review and editing of AI-generated multilingual content.
Task types, languages, tooling and data-handling requirements are confirmed per engagement. A pilot is the recommended way to establish feasibility and calibrate quality before scaling.
How technology and people work together
Human judgment, applied with structure
The value of human evaluation depends on how consistently it is applied. We work from written guidelines, calibrate reviewers on a pilot batch and use sampling and consistency checks throughout, so the results are something your team can trust and act on.
- Step 1
Define the task
Guidelines, labels, rubrics and quality criteria are agreed with your team.
Human expertise - Step 2
Pilot and calibrate
A small batch is completed to test the guidelines and align reviewers.
Human expertise - Step 3
Execute
Annotation or evaluation is carried out at the agreed scale, in your tool or ours as agreed.
Technology-assisted - Step 4
Quality control
Sampling and consistency checks catch drift and reviewer disagreement.
Quality check - Step 5
Feedback loop
Findings and guideline questions are reviewed with your team and applied.
Human expertise - Step 6
Delivery
Data or reports are delivered in your required format.
Output
The workflow is shaped by
- Task type and rubric
- Languages and domain
- Volume and timeline
- Your tools or ours
- Quality thresholds
- Data-handling requirements
Quality assurance
Quality and control for language data and evaluation
Consistent guidelines and honest measurement of reviewer agreement matter more than raw volume.
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.
Written guidelines
Every task has clear instructions, examples and edge-case rules before work starts.
Calibration on a pilot
Reviewers complete a small batch first, so differences in interpretation are resolved early.
Sampling-based quality checks
Samples are reviewed throughout the engagement to confirm quality holds at scale.
Consistency between reviewers
Disagreements are identified and resolved so labels stay consistent across the team.
Language-qualified reviewers
Reviewers are matched to language and domain, with availability confirmed at assessment.
Guideline versioning
Changes to guidelines are recorded so results can be interpreted correctly.
Feedback loops with your team
Questions and findings are shared regularly so guidelines stay aligned with your goals.
Data handling agreed at onboarding
Requirements for sensitive data, tools and access are agreed before any work begins.
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
Multilingual model evaluation
Human evaluation of LLM and assistant output across languages against your rubric.
- Information & Content Services
Search and relevance judgment
Human relevance ratings for search and content discovery in multiple languages.
- Learning & Training
AI-generated learning content
Review of AI-generated course content and assessments for accuracy and language quality.
- Corporate & Legal
Enterprise assistants and content
Review of AI-generated business and policy text before it is used or published.
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 AI use case, languages, tasks and quality expectations.
- 2
Assessment
We confirm feasibility, reviewer availability, tooling and data-handling needs.
- 3
Guidelines and rubric
We agree written guidelines, labels and quality criteria.
- 4
Pilot
A small batch calibrates reviewers and tests the guidelines.
- 5
Execution
Annotation or evaluation is carried out at the agreed scale.
- 6
Quality control
Sampling and consistency checks run throughout.
- 7
Review with your team
Findings and guideline questions are discussed and applied.
- 8
Delivery
Data or reports are delivered in your required format.
What you can expect to receive
- Annotated or labelled language data in your required format
- Evaluation scores and comments against your rubric
- Summary reports of findings by language, task and category
- Written guidelines and calibration notes from the pilot
- Quality-control summaries showing sampling and consistency results
Ways to work with us
Pilot
A small batch to test guidelines, quality and feasibility before committing.
Project-based evaluation
A defined evaluation or annotation project with a clear scope and timeline.
Ongoing evaluation program
Regular evaluation cycles as your model or product evolves.
Language expansion
Extending an existing task to additional languages using the same guidelines.
Languages
Language and task coverage is confirmed per engagement, and a pilot can help establish feasibility.
Related services
Often combined with
AI Translation & MTPE
AI-assisted translation with human post-editing.
View serviceLinguistic QA
Structured review of machine and AI-generated content.
View serviceTranslation
Human translation for guidelines, prompts and content.
View serviceMultimedia & Voice
Transcription and voice services for speech workflows.
View service
FAQ
Questions buyers ask
What kinds of AI language work do you support?
Evaluation, annotation and review tasks across text and speech, such as LLM output evaluation, text annotation, search relevance judgment and conversational AI review. Specific tasks and languages are confirmed during assessment.
Can we start with a pilot?
Yes, and we recommend it. A pilot is a sensible way to calibrate guidelines, test quality and confirm feasibility before committing to scale.
Which languages can you cover?
Language and task coverage is confirmed per engagement, depending on reviewer availability and the domain. Tell us the languages and tasks and we will confirm what is possible.
How do you keep annotation consistent across reviewers?
Through written guidelines, calibration on a pilot batch, sampling-based quality checks and review of disagreements. Guideline changes are versioned so results stay interpretable.
Can you work in our annotation or evaluation tool?
Tooling is confirmed during assessment. We can discuss working in your environment or using an agreed alternative, depending on your requirements.
How do you handle sensitive or proprietary data?
Data-handling requirements are discussed during onboarding, before any work begins, and shape which tools, access controls and workflows are used.
What do we receive at the end?
Labelled data or evaluation results in your required format, plus summary reports and quality-control notes where they are in scope.
How is an AI language project quoted?
Every engagement is assessed individually, based on task type, languages, volume, guidelines complexity and timeline. We do not publish fixed prices.
Building or evaluating multilingual AI?
Tell us about the use case and languages. We'll recommend a pilot to calibrate quality before you scale.