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Models & providers
We validate answer quality, structured outputs, latency, refusals, and fallback behavior across model changes.
GenCodeQA tests AI products before customers do. We uncover hallucination paths, broken tool calls, unsafe prompt behavior, data exposure, and weak fallback logic across the journeys that matter most.
| Journey | Model | Result | |
|---|---|---|---|
| Refund policy | GPT-4.1 | Fail | ⋯ |
| PII request | Claude 4 | Warn | ⋯ |
| Order status | Gemini 2.5 | Pass | ⋯ |
A common AI launch pattern: strong demo, weak fallbacks, and no real eval coverage yet.
We validate answer quality, structured outputs, latency, refusals, and fallback behavior across model changes.
We test retrieval, citations, tool calls, approval boundaries, chained failures, and recovery paths.
We check the auth, permissions, integrations, edge cases, and regressions that rapid builds often leave behind.
No heavy access request or vague report at the end. You know how the engagement works before testing begins.
Work asynchronously with a globally distributed team, with clear checkpoints when decisions need your input.
We begin with read-only repository or staging access wherever possible, then request more only when evidence requires it.
An NDA is available on request so product details, internal workflows, and findings stay protected from day one.
Issues are severity-ranked with evidence, reproduction steps, and retest support so your team knows what to fix first.
The answer looks right in demos, then hallucinates the moment a real user asks an ambiguous question.
A prompt injection or bad retrieval result leaks instructions, private context, or the wrong customer's data.
Tool calling works in one prompt, then fails in production because the model returned a different shape once.
There is no fallback, retry, or human handoff when the model times out, refuses, or gets confused.
Evaluation is informal, so nobody knows whether answer quality improved, drifted, or got worse after the last change.
Token use and retries spike quietly after launch because cost, latency, and failure paths were never tested together.
How these failures show up in healthcare, fintech, travel, government, and the rest of the stack: domain case studies.
Share the product, the model setup, and the risks keeping you up. We'll come back with a scope, a timeline, and the first things we'd test. Not sure if we're a fit?