Accuracy & groundedness
Gold-standard datasets built from your real cases, scored for correctness, completeness and faithfulness to source — so "good" stops being an opinion.
Your AI app isn't performing. We find out why.
You shipped an AI feature. It demos well, but users complain, answers drift, costs creep. We bring the discipline of software QA to AI systems — structured evaluation, hard numbers, and a prioritised path to fix it.
Sound familiar?
The symptoms we're called in for.
- — "It worked in the demo, but users don't trust the answers."
- — "Quality changed after a model or prompt update — we don't know why."
- — "It hallucinates on exactly the cases that matter most."
- — "Token costs tripled and nobody can explain where."
- — "We have no way of knowing if a change makes it better or worse."
Four dimensions. Hard numbers on each.
Reliability & regression
Automated eval suites that run on every prompt, model or pipeline change — catching regressions before your users do.
Safety & robustness
Structured red-teaming for prompt injection, data leakage, jailbreaks and harmful outputs — with reproducible findings, not anecdotes.
Cost & latency
Per-request cost and latency benchmarks across models and configurations — often the fastest win is the same quality at a third of the price.
Baseline → experiment → compare → report.
Step 1 — Baseline
We instrument your app as-is and establish the numbers: quality, failure modes, cost, latency.
Step 2 — Experiment
Controlled changes — prompts, retrieval, models, guardrails — each tested in isolation.
Step 3 — Compare
Every variant scored against the baseline. No change ships on vibes.
Step 4 — Report
A prioritised fix list with measured impact — plus the eval harness, which stays with you.
pytest
deepeval
ragas
promptfoo
LangSmith
Stop guessing. Start measuring.
A first evaluation report lands within two weeks — baseline, findings and fix list.