Quellit AI runs end-to-end web QA with a multi-agent engine. It reads a team's Jira, GitHub, and Figma requirements, writes the test cases from them, and then drives a real cloud browser to execute and verify each one.
The problem
Writing end-to-end tests by hand is slow, and they rot the moment the UI moves. What teams wanted was cases generated from the requirements they already write, then run against the real product rather than a mock.
What I built
Built a CrewAI multi-agent engine coordinating 20 LLM agents that turn Jira, GitHub, and Figma requirements into test cases, then drive a real cloud browser to execute and verify them. A usage-based credit ledger meters every AI test run and enforces quota at 11 entry points, refunding automatically when a run fails, on top of Stripe subscriptions and pay-as-you-go top-ups.
Results
Integrated 12 third-party systems, including a signature-verified GitHub webhook that turns every opened pull request into an AI-generated test plan. Linear, Vercel, and Figma OAuth are brokered through Nango.
