AI product reliability

The prototype proved the idea. Now prove the product.

A focused audit and stabilization path for AI-built SaaS and internal tools that reached production before their auth, data boundaries, webhooks, deployment, or error handling were ready.

What gets inspected

Identity and data boundaries

Authentication flows, authorization checks, row-level isolation, exposed secrets, and paths where one user may reach another user’s data.

External events

Webhook signatures, idempotency, retries, duplicate handling, partial failure, and whether a provider outage creates corrupt state.

Failure behavior

Swallowed errors, missing logs, unsafe fallbacks, concurrent requests, destructive actions, and whether production incidents can be reconstructed.

Deployment and recovery

Migrations, environment configuration, rollback path, monitoring, and the exact checks required before a release is accepted.

AI behavior

Prompt and tool boundaries, evaluation cases, model fallback, cost exposure, and when human approval is required.

The deliverable

A written diagnostic separates observed findings from hypotheses. Each finding includes severity, evidence, likely impact, a minimal remediation, and a verification step. Implementation is scoped only after the diagnostic.

Truth boundary

This is not a penetration test, legal compliance opinion, SOC 2 certification, or a claim that every defect can be found in a short audit. Those limits are stated before work begins.

Vibe-code rescue

The existing detailed offer, scope, and stabilization process remain available.

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Reliability engineering

Evaluation, observability, failure analysis, and acceptance testing for AI systems.

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Security hardening

Auth, permissions, secrets, and prompt-injection surface for AI-generated systems.

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