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.
01
5 min
Time to test results
02
3×
Release velocity improvement
03
Zero
Flaky test alert fatigue
04
80% less
Test maintenance reduction
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
01
LLM Feature Testing
Structured testing for LLM-powered features — summarisation accuracy, intent classification correctness, response relevance scoring, multi-turn conversation coherence.
02
Regression Tracking
Every model update benchmarked against your established quality baseline. Detect regressions before they reach production.
03
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.
04
Security Testing
Systematic adversarial testing of your AI feature attack surface — prompt injection, jailbreak attempts, data exfiltration probes, context manipulation, and system prompt leakage.
05
Performance
LLM inference latency under real user load, streaming performance, token throughput, and rate limit behaviour.
06
Production Monitoring
Real-time monitoring of AI feature quality in production — output quality scoring, anomaly detection, user satisfaction signal correlation.
Next step
Bring QE for AI SaaS Products to your stack
Scope it in one call — outcomes defined upfront, free assessment included.