LLM evaluation services
We build the evaluation setup that tells you whether an LLM feature is good enough to release and whether the next change makes it better or worse. Your team can switch models, rewrite prompts and add features with evidence behind each decision.
What it is and when it fits.
LLM evals are automated tests for AI behaviour. We build datasets from real usage and expert-labelled examples, extend them with synthetic cases for rare situations, and score outputs with deterministic checks, model-graded rubrics and human review where needed. The suite runs in CI as a regression gate and keeps running in production through sampling and monitoring.
Evals are worth the effort as soon as an LLM feature affects customers, money or compliance, or when several people change prompts and models. For a one-off internal script they are overkill. We also add evals to existing systems built by other teams, which is often the fastest way to find out why quality feels inconsistent.
What we build.
Evaluation datasets
Curated test cases from production logs and domain experts, labelled with expected outcomes and grouped by scenario.
Synthetic test sets
Generated questions, documents and edge cases that cover rare situations, reviewed by people before they enter the suite.
Model-graded checks
LLM-as-judge rubrics for faithfulness, tone and completeness, calibrated against human ratings so the scores mean something.
CI regression gates
Eval runs on every pull request that block releases when accuracy, safety or cost moves past the thresholds you agree.
Production monitoring
Tracing, sampled scoring of live traffic, drift alerts and dashboards for quality, latency and spend per feature.
How it works.
- 01
Define what good means
We work with your domain experts to turn quality into specific, scoreable criteria for each task the system performs.
- 02
Build the dataset
We assemble real and synthetic cases, label them and establish a baseline score for the current system.
- 03
Wire it into delivery
The suite runs in CI and on model or prompt changes, with results your team can read without opening a notebook.
- 04
Keep it current
Production failures and user feedback are added as new cases, so the eval set tracks how the product is actually used.
Related work.
Built with.
All technologiesFurther reading.
LLM evals: how to test AI features before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
How to evaluate RAG: retrieval metrics, faithfulness and golden sets
How to measure a RAG system properly: separate retrieval from answers, check faithfulness claim by claim and build a golden set you can trust.
4 min read
Common questions.
Yes, and it is a common starting point. We connect tracing, build a dataset from your logs and a baseline score, and you get a clear picture of where the system fails before deciding what to fix.
They are when they are calibrated. We compare judge scores with human ratings on a sample, tighten rubrics where they disagree and keep deterministic checks for anything that can be verified exactly, such as fields, formats and citations.
It depends on how many distinct scenarios the system handles. A focused set of well-labelled cases per scenario is more useful than thousands of random examples, and the set grows as production shows new failure modes.
Yes. Running the same suite across models shows the trade-offs in quality, latency and cost for your task, which makes switching providers or moving to an EU-hosted or open model a measured decision.