Release Risk Gate
Continuous AI-QA and Agentic QE release gates. Clear ship/no-ship evidence on every release. For teams shipping AI features weekly.
Who It Is For
- You ship AI features at high cadence (weekly or faster)
- You have multiple AI features in production
- You need release decisions backed by evidence, not engineering judgment
- You want executive-level reporting on AI quality risk
- You have outgrown one-off testing and need a continuous quality function
- You are not ready to hire a full internal AI-QA team
Releasing AI features without a gate is releasing risk
Engineering teams shipping AI features weekly face a structural problem. Manual review does not scale. Eval suites built once and never updated drift out of relevance. Quality reviews become checkbox exercises. The team starts shipping based on "the AI feels okay" rather than evidence.
This produces a slow-motion failure mode. Hallucination rates creep up. RAG quality degrades. Prompt regressions accumulate. Customer complaints rise gradually.
Release Risk Gate prevents this. Every release is evaluated against a continuously maintained eval suite. Failures surface as risk evidence. Successes ship with documented confidence.
What You Get
| Deliverable | Description |
|---|---|
| Foundation services | Everything in AI-QA Foundation, maintained continuously |
| Ship/no-ship dashboard | Real-time view of release readiness per feature |
| Automated red-team probes | Refreshed monthly |
| Continuous eval maintenance | Eval suite kept current as models, prompts, and data change |
| Monthly executive risk reports | Written reports for CTO and engineering leadership |
| Release support | Available during critical AI feature releases within agreed coverage hours |
| Incident response | Defined response approach based on engagement tier |
| Quarterly methodology refresh | Eval categories and red-team probes updated as AI ecosystem evolves |
How It Works
Step 01: Foundation setup
Day 0-30: foundation setup, gate integration, baseline eval suites, dashboard configuration.
Step 02: Continuous operation
Day 31 onwards: continuous operation. Weekly eval maintenance. Monthly probe refresh. Monthly executive report. Quarterly methodology review.
Step 03: Pod and scale
Typical pod: senior engineers plus the practice lead as engagement lead. Scales with AI features under management.
Investment
Release Risk Gate is scoped after reviewing your release cadence, number of AI features, current eval coverage, CI/CD workflow, and reporting needs.
You receive a private monthly engagement proposal with clear scope, operating cadence, team model, and commercial terms.
Success Metrics
Your team ships AI features faster, not slower. The gate catches issues automatically.
Hallucination rate, prompt regression rate, RAG quality scores trend in the right direction month over month.
Engineering leadership has answers to "How risky is this release?" with evidence.
Sample Deliverable
Continuous outputs: real-time dashboards, automated reports, monthly executive summaries, quarterly methodology updates.