Build a reliable AI workflow for your SaaS team.

A B2B SaaS AI integration should improve one customer or internal workflow without weakening tenant isolation, permissions, or the product's existing source of truth. The goal is a bounded feature that customers can use and the product team can operate after handover.

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.

Custom AI Workflow Integration is $10,000 to $35,000, milestone-based. The final scope depends on the defined workflow, system access, evidence required, and agreed handover.

Retrieval and actions stay inside the correct tenant Generated output is validated before it reaches the product surface Usage, cost, and failure states are observable 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 work scopes one product workflow and its handover. It is not an unlimited AI feature roadmap or replacement for product ownership.