Make your SaaS AI safe enough to launch.

A SaaS AI feature needs representative evaluation before a prompt, model, or retrieval change reaches customers. Reliability means measuring answer quality, tool use, tenant boundaries, latency, cost, and escalation under the cases customers actually create.

Yes. The review identifies the bugs, architecture gaps, deployment issues, monitoring gaps, and edge cases that could block a trustworthy release. Implementation is separately scoped.

Yes. It checks identity, authorization, tenant boundaries, RLS where relevant, secrets, sensitive data exposure, API abuse, and prompt or context manipulation when AI behavior is in scope. It is a bounded readiness review, not a penetration test.

Yes. A senior engineer traces the relevant code and configuration, then validates behavior against evidence, including hidden logic defects, unsafe migrations, weak permissions, retry failures, and duplicate actions.

Yes. We inspect existing tests, pull-request checks, CI/CD, staging, monitoring, deployment, and rollback controls that affect the reviewed journey. We do not implement every gap in the review fee.

Yes. APIs, webhooks, payments, CRM and automation workflows, document processing, agents, media pipelines, queues, retries, and recovery are checked when the critical journey depends on them.

Yes. The review is platform-agnostic and can inspect apps built with Lovable, Replit, Base44, Cursor, Claude Code, Codex, v0, Bolt, WordPress, or similar tools. Migration planning or implementation is separately scoped.

Yes, where it affects the reviewed journey. We check architecture, database design, reusable components, documentation, discoverability, platform lock-in, ownership, and the next developer's ability to make a safe change.

Yes, when they affect the launch journey. We check responsive behavior, loading, empty and error states, accessibility, SEO-critical surfaces, and launch-impacting product polish. A full redesign is outside scope.

AI Reliability Sprint is $4,500 for one defined workflow, with an optional $1,500 per month retainer. The final scope depends on the defined workflow, system access, evidence required, and agreed handover.

Prompt or model changes do not regress critical cases Tenant and permission checks run outside the model Failures are observable, bounded, and reversible The engagement should end with an explicit handover and a clear list of remaining risks, not a general claim that the AI is safe.

The sprint hardens one defined workflow. It does not certify the entire product or promise that a probabilistic system will never fail.