Continuous AI-QA Operations
Ongoing AI-QA and Agentic QE coverage without building a full internal AI-QA team. Premium managed service for AI-native organizations.
Who It Is For
- AI features are core to your product, not edge cases
- You ship AI changes weekly or daily
- You need continuous AI-QA capability but cannot economically hire a full internal team
- You require executive-level reporting suitable for leadership or board visibility
- AI quality is a strategic risk, not just an engineering concern
The cost of building internal AI-QA capacity
Hiring a dedicated AI-QA team internally is slow, expensive, and difficult because the role sits across QA automation, evals, red-teaming, observability, and AI product risk. Most organizations need this capability now, not in 12-18 months.
Continuous AI-QA Operations provides the function without the build cost. We operate as your AI-QA team. Embedded in your release process. Owning eval coverage. Running continuous red-team probes. Producing executive reporting.
What You Get
| Deliverable | Description |
|---|---|
| Everything in Release Risk Gate | Continuous eval maintenance, gates, red-team probes, reports |
| Custom test framework development | Bespoke eval categories, scoring rubrics for your AI features |
| Embedded engineering presence | Senior engineers operating as part of your engineering org |
| Coverage during agreed working windows | AI quality oversight during support hours defined in scope |
| Executive reporting where needed | Quarterly summaries for leadership and, where required, board visibility |
| Competitor model benchmarking | Quarterly comparison against industry alternatives |
| Priority incident support | Defined response approach within agreed coverage windows |
| Methodology evolution | Continuous updates included in scope |
How It Works
Step 01: Embedded model setup
First 60 days establish the embedded model. Foundation setup, gate integration, eval suites, dashboard configuration, reporting cadence agreed.
Step 02: Ongoing operation
Continuous operation from day 61 onwards. Weekly sync. Monthly executive reports. Quarterly leadership summaries. Methodology updates included.
Step 03: Pod and scale
Pod of senior engineers plus the practice lead as engagement lead. Scales with AI features in scope. Can expand or contract based on product roadmap.
Investment
Continuous AI-QA Operations is available after assessment. Scope depends on AI feature count, release cadence, coverage expectations, support windows, reporting needs, and whether the engagement requires a dedicated pod.
You receive a private managed-service proposal after discovery.
Success Metrics
AI quality function operates at internal-team caliber without internal-team cost.
Leadership receives structured reporting on AI quality risk on appropriate cadence.
AI features scale in count, capability, and risk profile without proportional growth in quality concerns.
Sample Deliverable
Continuous outputs: real-time dashboards, automated reports, monthly executive summaries, quarterly methodology updates, competitor model benchmarking reports. Anonymized sample architecture available on request.