Reliability
AI Evaluation & Reliability
Most teams ship an AI feature and then have no way to tell whether a prompt change made it better or worse. Evaluation is the difference between an AI feature you can improve and one you can only nervously leave alone. It is also the part almost nobody builds, which is why it is where we spend our time.
- Vitest
- Playwright
- PostgreSQL
- TypeScript
- Python
What this includes
Each of these exists because leaving it out is how these systems fail in production.
Production transcript export
A privacy-safe pipeline that exports real conversations, scoped to a single organisation and redacted of personal data, so you can do error analysis on what users actually did rather than what you imagined they would.
Failure taxonomy
Open-coded error analysis turned into a written, shared vocabulary for how your system fails. Once failures have names, they can be counted, prioritised, and argued about productively.
Deterministic tool evals
For agents, the highest-signal test is not "was the prose good" but "did it call the right tool with the right arguments". Those assertions are cheap, fast, and run like ordinary tests.
Regression suites in CI
A graded set of cases that runs on every change, so a prompt edit that quietly breaks an edge case fails the build instead of reaching production.
PII-safe
Redacted, org-scoped transcript export
Written
Failure taxonomy, not tribal knowledge
Tool + args
Deterministic assertions, not vibes
Where we have done this
Next step
Tell us what you’re trying to build
Most engagements start with a fixed-price audit, so the first thing you buy is a decision rather than a commitment.