AI evaluation services
Native speakers and subject experts score answers, check facts and test agents in each language, so you know how your model performs before your users do.
| Measure | English | Spanish | Hindi |
|---|---|---|---|
| Answer quality | Better | Better | Same |
| Factual accuracy | Same | Better | Worse |
| Grounding in sources | Better | Same | Same |
| Safety | Same | Same | Worse |
Illustrative example. Measures and languages are set per project.
- We evaluate
- LLMs, RAG systems, agents, chatbots, MT engines and search
- Reviewers
- Native speakers and subject experts, calibrated on your rubric
- Methods
- Rubric scores, side-by-side comparison, claim checks and adversarial tests
- Output
- Labeled data, scores by language and failure reports with examples
What human AI evaluation is for
AI evaluation is measuring how well a model or AI product does its job: whether answers are correct, useful, grounded, safe and natural for the user. Automated metrics and LLM judges are fast, but someone has to check that they agree with people, and they are weakest in languages other than English. Human evaluation gives you that reference.
Teams use it to choose between models, decide whether a release is ready, find out which languages lag behind, and build the test sets they run on every new version.
Evaluation services
Each has its own page with examples and a sample process.
- LLM evaluationAnswers scored against your rubric for accuracy, helpfulness, fluency and fit.
- RAG evaluationRetrieved passages judged for relevance, and every claim checked for support.
- AI agent evaluationAgent runs reviewed step by step: plans, tool calls, outcome and safety.
- Chatbot and voice assistant testingNative speakers talk to your bot like customers and log each failure.
- Custom LLM benchmarksPrivate test sets with checked answer keys, written natively per language.
- Fact-checking and hallucination reviewAnswers split into claims, each checked against reliable sources.
- Red teaming and safety reviewAdversarial prompts in each language, with a severity-rated report.
- Machine translation evaluationMT and LLM translation scored by professional translators.
- Search relevanceSearch and recommendation results rated by people in each market.
Which evaluation fits your question
Most teams start with one and add others as the product grows.
- Is the new model better than the old one?Side-by-side LLM evaluation on your own prompts, per language.
- Does our assistant make things up?Fact-checking for open answers, RAG evaluation when it answers from your documents.
- Can the agent finish real tasks?Agent evaluation of full runs, step by step.
- Will customers in a new market be happy with it?Chatbot testing and AI localization review by native speakers.
- Is it safe to launch?Red teaming in each language your users write in.
- How do we track quality over time?A private benchmark you run on every release.
How an evaluation project runs
Calibration comes before any scores you rely on.
Questions
What you need to decide, the models, languages and data.
Rubric
Measures, scales and examples agreed, or written with you.
Calibration
Reviewers score a shared sample until agreement is good enough.
Agreement check QAEvaluation
Blind review, with overlap to keep measuring agreement.
Report
Scores by language and measure, failure examples and the raw labels.
Questions about AI evaluation
What is AI evaluation?
AI evaluation is measuring how well an AI model or product performs, for example whether its answers are accurate, helpful, grounded in sources and safe. Human evaluation uses trained reviewers to score outputs against a rubric.
Why use human evaluators instead of automated metrics?
Automated metrics and LLM judges need a human reference to show they can be trusted, and they miss subtle errors, especially outside English. Most teams use human evaluation to set that reference and to check decisions that matter.
Can you evaluate models in several languages?
Yes. Native-speaker reviewers evaluate each language, and results are reported per language so you can see which markets lag behind.
Can your labels be used to train an LLM judge?
Yes. Calibrated human scores are the data you need to train or check an automated judge, and we deliver them with reviewer agreement figures.
How do we start?
Book a call or send a sample of prompts and outputs with your rubric. We propose a pilot, score it with calibrated reviewers and show you the agreement before you commit to volume.
Plan an evaluation
Tell us what you need to decide, the models and the languages, or book a call. We propose a pilot and show you reviewer agreement first.
- Phone
- +44 20 3290 0116
- Phone hours
- Weekdays 9:30 to 16:00, weekends 9:30 to 12:00, UK time