AI reliability for IT, Marketing & Sales Agencies
Your team can build AI demos quickly, but client projects stall when integrations, testing, and production controls are missing.
Repeatable delivery without hidden production risk · $4,500 sprint · One defined workflow
Representative release gate
agency deployment gate
Release decision
Controls required before approval
High-impact path
A failed tool call must have an intentional retry, fallback, or handoff.
Evaluate
Test set
Control
Approval
Recover
Owner
Direct answer
IT, Marketing & Sales Agencies · AI Reliability Sprint
Agencies need repeatable evaluation and release controls before they can responsibly ship similar AI features across clients.
Launch gate
One named journey
01 · Critical journey
/One client-facing agent or RAG workflow that the agency expects to operate or reproduce.
Launch gate
Where risk concentrates
02 · System boundaries
/Tracing, release, and support workflow
Launch gate
What changes the call
03 · Evidence required
/Define rollback, escalation, and ownership after handover
Important boundary
The sprint hardens one reusable delivery pattern. Each materially different client workflow still needs its own acceptance criteria and evidence.
Read the full AI Reliability Sprint scopeQuestions
Agencies need repeatable evaluation and release controls before they can responsibly ship similar AI features across clients. Reliability work should expose which behaviours are shared, which are client-specific, and how regressions are caught before an update reaches production.
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.
Separate platform regressions from client-data problems Test permission and retrieval boundaries for each tenant Define rollback, escalation, and ownership after handover 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 reusable delivery pattern. Each materially different client workflow still needs its own acceptance criteria and evidence.
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