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PrimeCompass.ai
AI-Powered quality intelligence from live application
36 followers
AI-Powered quality intelligence from live application
36 followers
PrimeCompass auto-discovers test scenarios by watching real applications run — no manual recording, no LLM hallucinations. It produces executable Playwright scripts grounded in captured DOM events, with non-bypassable safety gates, dataset-driven test data, cross-release drift detection, and built-in defect lifecycle. Recorders need humans; AI-generators invent. PrimeCompass earns its tests by observing.
Mailwarm
How does it handle dynamic apps where selectors and DOM structure change between releases?
@othman_katim PrimeCompass AI is designed to handle dynamic applications where selectors and DOM structures change frequently between releases.
Instead of relying only on static selectors, the platform continuously maps application behavior and understands UI patterns using AI-driven exploration. It combines multiple signals such as element attributes, structure, context, navigation flow, and behavioral relationships to identify elements more reliably across UI changes.
This helps reduce brittle test cases and minimizes maintenance effort when applications evolve between releases. The platform can automatically regenerate and adapt Playwright + Cucumber test flows based on the latest application behavior, helping teams detect behavioral drift early and keep automation suites resilient.
🌐 https://www.primecompass.ai/
🔐 https://app.primecompass.ai/login
🎥 https://youtu.be/E6FXYnniELo?si=Qgd6pOq6pOlz3zpZ
Most AI testing tools either depend on manual recording or generate tests that “look right” but break in real-world environments.
We started PrimeCompass with a simple belief:
Quality intelligence should come from observing actual application behaviour — not assumptions, prompts, or hallucinated flows.
PrimeCompass continuously watches applications run, discovers real user journeys, detects drift across releases, and generates executable Playwright tests grounded in captured runtime evidence.
Our focus is not just faster QA.
It’s building confidence in modern AI-accelerated software delivery.
Would genuinely love feedback from the Product Hunt community — especially from teams dealing with:
• Release confidence issues
• QA scalability challenges
• AI-generated code validation
• Regression drift
• Compliance & auditability in CI/CD
Happy to answer any questions and discuss where software quality engineering is heading next 🚀