AI-Powered QA & Model Testing
Combine ATTEST’s QA heritage with AI: self-healing test automation for apps plus rigorous accuracy, bias, and hallucination evaluation for LLM features.
Bulletproof Reliability for Both UI Apps and AI Systems
Traditional pass/fail assertions break when UIs shift slightly or when non-deterministic LLM outputs vary between runs. ATTEST bridges deep QA engineering with modern AI evaluation.
We upgrade fragile Selenium/Playwright suites with resilient locator strategies and run continuous automated LLM benchmark harnesses checking faithfulness, latency, and prompt-injection safety.
How We Assure AI & App Quality
Automated quality gates built specifically for AI-enabled products and high-velocity CI/CD teams.
Smart Test Automation
- AI-assisted test case generation from PRs & user stories
- Resilient self-healing UI locators reducing flaky CI failures
- Visual regression diffs across responsive desktop & mobile screens
LLM & RAG Evaluation Suites
- Automated hallucination, groundedness & context-recall scoring
- Golden dataset regression testing before prompt or model upgrades
- Structured JSON & schema compliance checks in CI pipelines
Red-Teaming & Safety Audits
- Adversarial prompt-injection and data-leakage penetration tests
- Token-budget stress testing and fallback timeout verification
- Clear QA sign-off reports for compliance and enterprise buyers
AI QA & Model Testing FAQ
Ensuring predictable quality across non-deterministic AI features and rapid UI releases.
Yes. We build independent golden-question evaluation suites and red-team test packs to benchmark accuracy, refusal behavior, and latency before release.
They enhance them. We integrate resilient locator strategies and visual checks directly inside your existing Selenium, Playwright, or Cypress CI/CD pipelines.
You get automated evaluation scripts integrated into GitHub Actions/Jenkins, hallucination/precision scorecards, defect logs, and reproducible regression datasets.
Shipping an AI Feature or Scaling QA Automation?
Schedule an AI QA assessment with our testing architects to eliminate flaky tests and catch model regressions before production.