Solution

QE for AI SaaS Products

AI features break differently. Your QE strategy must too.

Traditional QE verifies deterministic behaviour — same input, same output, every time. AI SaaS features are probabilistic. Your test strategy needs to verify that outputs are correct within a tolerance range, safe across adversarial inputs, and consistent in quality as your underlying model changes.

For:CPOVP EngineeringHead of QAAI Product Manager

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The challenge

What makes this hard

Your existing QE process was built for software that doesn't change by itself.

No framework for testing probabilistic outputs

Model updates silently degrade quality

LLM feature regressions invisible until customer complaints

Prompt injection vulnerabilities untested

AI workflow chains fail in non-obvious ways

What we deliver

The Qapitol approach

LLM Feature Testing

Structured testing for LLM-powered features — summarisation accuracy, intent classification correctness, response relevance scoring, multi-turn conversation coherence.

Regression Tracking

Every model update benchmarked against your established quality baseline. Detect regressions before they reach production.

Agentic QE

Self-healing test suites that adapt as your SaaS product evolves. Agent Fabric deploys AI execution agents that generate, run, and repair tests automatically.

Security Testing

Systematic adversarial testing of your AI feature attack surface — prompt injection, jailbreak attempts, data exfiltration probes, context manipulation, and system prompt leakage.

Performance

LLM inference latency under real user load, streaming performance, token throughput, and rate limit behaviour.

Production Monitoring

Real-time monitoring of AI feature quality in production — output quality scoring, anomaly detection, user satisfaction signal correlation.

Bring QE for AI SaaS Products to your stack

Scope it in one call — outcomes defined upfront, free assessment included.

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