AI-QA Foundation
Build the first serious quality layer for your LLM, RAG, or AI-powered feature. Evals, automation, CI gates, and reporting. Timeline scoped to your feature complexity and team capacity.
Who It Is For
- You have one or two AI features in production or about to ship
- You have no formal eval system yet, or what you have is unstructured
- You need release confidence before scaling AI feature count
- You have engineering capacity to integrate what we build, but not to build it yourselves
- You are willing to commit to a focused engagement scoped to your feature complexity
The cost of shipping AI features without a foundation
Most teams ship their first AI feature using manual testing and prompt engineering judgment. That works for the first release. It breaks by the third.
Without an eval foundation, every prompt change becomes a coin flip. Every model upgrade becomes a regression risk. Every RAG data refresh creates silent quality drift.
AI-QA Foundation builds the structural quality layer. After this engagement, your team has repeatable evals, automated test execution, CI-integrated gates, and clear reporting.
What You Get
| Deliverable | Description |
|---|---|
| Production eval suite | LLM evaluation tests covering accuracy, hallucination, prompt regression, edge cases |
| RAG quality tests | Retrieval quality assessment, grounding checks, source attribution validation |
| Hallucination detection logic | Automated detection of ungrounded claims, fabricated facts |
| CI/CD integration | Tests wired into GitHub Actions, Jenkins, or your CI platform |
| Test data management | Versioned eval datasets, expected outputs, scoring rubrics |
| Reporting dashboard | Run history, score trends, regression alerts |
| Documentation | Runbook, methodology guide, internal handover documentation |
| Engineering handover | Two-session knowledge transfer to your team |
How It Works
Phase 1: Discovery
Map the target AI feature in detail. Written scope and prioritized eval categories delivered before build begins.
Phase 2: Foundation build
Build eval suite, integrate into CI, validate against historical examples. Regular demos throughout the build.
Phase 3: Validation and tuning
Run suite against real production scenarios. Tune thresholds based on your risk tolerance and release requirements.
Phase 4: Handover
Documentation finalized. Two engineering handover sessions. Your team owns the system.
Investment
AI-QA Foundation is scoped after the Free AI-QA Maturity Audit. Pricing depends on the AI feature complexity, current test maturity, CI/CD setup, RAG scope, and handover requirements.
After the audit, you receive a fixed-scope proposal covering timeline, deliverables, team structure, and commercial terms.
Success Metrics
Your CI blocks releases when the eval suite fails. Not a notification. A blocked merge.
Your team can confidently change prompts, swap models, or update RAG data, knowing the eval suite will catch quality drift before customers do.
Your engineering leadership can answer the question "is this AI feature safe to ship today?" with evidence, not opinion.
Sample Deliverable
Working code repository. Eval suite. CI workflow files. Test datasets. Documentation. Reporting dashboard. Anonymized sample architecture available on request.
FAQ
Build a real quality layer for your AI features.
Scoped to your situation. Production-ready output. Your team owns it after handover.