AI Reliability & Evaluation
Make AI behavior measurable before it reaches users: evaluation suites, regression gates, observability and a real failure taxonomy.
Sounds familiar?
- 01The demo was impressive; the production behavior is not.
- 02Nobody can say whether last week's prompt change made things better or worse.
- 03Failures reach users before anyone notices them internally.
- 04Quality disputes are settled by opinion instead of measurement.
What it costs to leave it
- Support burden and trust erosion grow with every silent regression.
- Model, prompt, or vendor changes become too risky to make at all.
- The team optimizes what is easy to see, not what users actually experience.
What the engagement covers.
An evaluation dataset built from your real tasks and real failure cases.
Automated evaluation pipelines wired into CI so regressions block release.
LLM-as-judge scoring calibrated against human review, not substituted for it.
A failure taxonomy: what breaks, how often, with what user impact.
Observability for quality, latency and cost on every production request.
How the engagement runs.
Inventory current behavior, failure reports and change history.
Build evaluation sets from real tasks, edge cases and incidents.
Wire evaluation into CI and release decisions.
Dashboards, alerting and review loops the team can own.
Deliverables
Evaluation dataset and methodology documentation
Automated evaluation pipeline integrated with CI
Regression gate configuration for releases
Failure taxonomy and incident review templates
Quality/latency/cost observability dashboards
A good fit when
- Teams whose AI feature is already live or about to be.
- Teams preparing a model, prompt or vendor migration.
- Teams whose leadership asks “how do we know it still works?”
Probably not, when
- Pre-product ideas looking for a proof-of-concept demo.
- Teams unwilling to invest in curated evaluation data.
Common questions.
Can you work with an existing engineering team?
Yes. Most reliability work is done with your team, using your CI and your stack. The goal is a system your engineers own, not a consultancy-owned test harness.
Is LLM-as-judge trustworthy?
It is useful when calibrated. We validate judge behavior against human-labeled samples, track judge drift, and never let it be the only signal.
Can you audit an existing AI system?
Yes. An audit produces a prioritized reliability report: failure modes, measurement gaps, and the specific changes with the highest expected impact.
Do you need access to production data?
No. Evaluation datasets can be built from representative samples, synthetic variants, or anonymized logs, depending on your constraints.