AI evaluation & reliability
We turn observed failure modes into versioned datasets, calibrated graders, and release gates, so model, prompt, retrieval, and tool changes can be tested before they reach users.
A support agent cites an expired return policy. The response reads well, but the retrieved evidence, answer, and release decision need separate checks.
Inside the system
Inspect each stage of a support-agent evaluation. The records below are illustrative artifacts, not benchmark results.
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Follow the architecture as you scroll, or select a layer.
Review traces with domain experts. Separate retrieval errors, unsupported answers, and incorrect tool use; label representative cases and keep a held-out set for regression testing.
Run baseline and candidate against the same dataset version and controlled context. Record retrieved documents, tool arguments, outputs, latency, and usage; repeat stochastic cases where needed.
Use code for schemas, citations, and tool permissions. Use rubric-based judges for semantic quality, calibrate against expert labels, and investigate disagreements before trusting the grader.
Compare results by failure slice against agreed gates. Block critical regressions; review sampled production traces and turn confirmed failures into new tests for the next release.
Inspect the behavior
Synthetic support policy: unopened items can be returned within 30 calendar days of delivery. Switch cases to inspect the evidence and gate; these are fixtures, not measured evaluation results.
Illustrative system · Synthetic inputsAn unopened item arrived 12 days ago. Can I return it?
The answer checks the unopened condition and cites the current 30-day policy. Release still depends on the complete regression suite and agreed gates.
Verification specification
We turn expected behavior into repeatable tests and operating controls. Acceptance thresholds are agreed for your workflow, data, and the consequences of an error.
Measure cases meeting all required assertions, with denominators and separate results for stale evidence, missing context, and tool errors.
Agreed thresholds per slice; critical violations block release.
Compare judge labels with held-out expert labels. Inspect false passes, false failures, and the traces behind disagreements.
An uncalibrated judge cannot be the sole release gate.
Compare baseline and candidate on the same cases; report repeated-run variability, p95 latency, and attributed cost per successful task.
Review regressions against agreed quality and operating budgets.
The handover
A defined implementation, reviewable evidence, and an operating model your team can own.
How we workFailure taxonomy, representative fixtures, reference labels, dataset splits, and reproducible run configuration.
Code assertions, judge rubrics, expert calibration examples, trace comparisons, and CI checks.
Sampling rules, review ownership, trace retention settings, and a path from confirmed failure to regression test.
Architecture decisions
It depends on the task. A retrieval system can be checked for whether it finds the required evidence; an agent for correct tool selection and task completion. We combine deterministic checks, human review, and calibrated model-based grading where useful, with latency and cost measured alongside quality.
Yes. We review the available code, datasets, traces, and deployment history, then establish a baseline. The assessment identifies changes your team can prioritize, and we can implement the agreed fixes within the existing architecture.
Talk to an engineer
Bring a workflow, a product brief, or an AI system that needs to ship.
