What problem does it solve? As a product manager and founder myself, I constantly faced issues releasing a new feature only to discover critical bugs or that the build doesn't fully meet acceptance criteria. Bottlenecks in our QA and release process—tickets and issues stuck in manual testing, slow deployments due to delayed verification, and missed acceptance criteria leading to bugs slipping into production. I built Quell to automate these tedious steps, freeing up teams to focus on actual feature development and faster iterations.
How is it different?
Integrates with existing dev workflows—triggers tests via Jira/Linear issue status automatically testing Vercel, or Netlify or other URL deployment builds.
Tests against explicit acceptance criteria pulled directly from your issue tracker.
Current Capabilities (free to test):
Automatically trigger QA runs from Linear/Jira issue state transitions or Vercel/Netlify deploy previews.
Generate immediate test reports and tickets for issues spotted.
Quell is ready to test right now—email only required to try out demo functionality directly:
Try out [Quellit.ai](http://quellit.ai/) for free
I'm actively iterating based on user feedback—would love to hear your thoughts, suggestions, or even criticisms on the idea, implementation, integrations, or anything else.
Thanks for checking it out!
fazlerocks•8mo ago
For alternatives in this space, there's qawolf (https://qawolf.com) for similar automated testing workflows, or I'm actually building bug0 (https://bug0.com) which also does AI-powered test automation, still in beta. For the more established players there's always Cypress (https://cypress.io) and Playwright (https://playwright.dev) if you want to stay closer to code, or TestRail (https://testrail.com) + Browserstack (https://browserstack.com) for the enterprise route.
Will definitely try the demo - the acceptance criteria parsing sounds like it could catch a lot of edge cases that usually slip through.