Know what your model gets wrong before your customers and regulators do.
We build evaluation suites for your specific use case, run them against the models you are considering or already shipping, and give you a readout your risk and compliance teams can use.
Outcomes we deliver.
Task-specific eval suites
Golden datasets and scoring rubrics built around your real inputs and the failure modes that would actually hurt you.
Calibrated LLM-as-judge pipelines
Automated grading at scale, calibrated against human reviewers so you know how far to trust the automated score.
Red-teaming and safety testing
Adversarial prompts, jailbreak attempts, data-leakage probes and policy violations, run systematically and reported with reproduction steps.
Bias and fairness checks
Performance sliced by the groups and scenarios that matter for your sector, with clear reporting of where behaviour differs.
Regression evals in CI
The suite runs on every prompt, model or data change, so a quiet regression never reaches production.
Model selection bake-offs
Side-by-side comparison of candidate models on your data, with cost and latency alongside quality, so the choice is evidence-based.
Scores by category, with every failure reproduced.
Illustrative structure only. Real readouts contain your categories, your numbers and the exact inputs that failed.
| Category | Result | Status |
|---|---|---|
| Task accuracy | Scored · example | ✓ pass |
| Policy adherence | Scored · example | ✓ pass |
| Adversarial robustness | Scored · example | ! review |
| Group consistency | Scored · example | ✓ pass |
| Data leakage probes | Scored · example | ✕ fail |
Deliverables you own.
- Evaluation harness committed to your repository
- Golden dataset with provenance and review notes
- Scored report with per-category results and examples of failures
- Risk register with severity, likelihood and recommended mitigations
- Readout session for engineering, risk and compliance
Four steps, measured at each one.
Define
Agree what the model must do, what it must never do, and how each will be scored.
Build
Assemble the dataset and the harness, calibrate judges against human labels.
Run
Evaluate candidates, red-team the chosen one, slice the results.
Report
Deliver the scores, the failures and the register, then wire the suite into CI.
What we will and won’t do.
What we work with.
Chosen per engagement, never by default. No vendor logos because we are not paid by any of them.
Who this is for.
- Teams about to ship an LLM feature in a regulated product
- Risk and compliance functions that need evidence, not demos
- Companies choosing between models or vendors
Common questions about ai model evaluation.
What exactly gets evaluated?
Whatever your use case depends on: accuracy on your task, adherence to policy, refusal behaviour, robustness to adversarial input, consistency across groups, latency and cost. We agree the list and the scoring before we start.
How are automated judges calibrated?
A sample of outputs is scored by your domain experts. We measure agreement between the automated judge and the humans, tune the judge until agreement is acceptable, and report that agreement rate alongside every automated score.
How often should we run evaluations?
On every change to the prompt, the model, the retrieval data or the guardrails. That is why the suite goes into CI. A full red-team pass is typically repeated before major releases or when the threat model changes.
Does this help with the EU AI Act or model-risk reviews?
It produces the kind of evidence those processes ask for: documented test design, results, known limitations and mitigations. It does not make you compliant on its own; your assessors decide that. We write the report so they can use it directly.
What drives the cost?
The number of distinct behaviours to test, how much human labelling is needed to calibrate, and whether red-teaming is in scope. A fixed-price evaluation audit covers one use case end to end.
Tell us what you're trying to ship.
Thirty minutes with the founder. We'll tell you honestly whether we're the right team, and what it would cost.