I built an AI-powered QA platform because manual testing tools haven't kept up — launching on Product Hunt today

A developer built Evaficy Smart Test, an AI-powered QA platform for manual testing teams, launching on Product Hunt today. The platform covers the full manual QA workflow with AI test case generation, expert validation, test runs, defect tracking, and AI risk insights. It is built with React and Material UI, with Stripe for subscription billing.

For the past year and a half, I've been building Evaficy Smart Test — a QA platform for manual testing teams that brings AI into the parts of the workflow that are still painfully manual: writing test cases, tracking validation, and figuring out what to test before a release. We're launching on Product Hunt today, and I wanted to share the story behind it, plus some of the technical decisions along the way. The problem Most QA tooling falls into two buckets: traditional test management tools think spreadsheets with extra steps , or newer AI tools that focus almost entirely on test automation — generating Selenium/Playwright scripts, self-healing locators, that kind of thing. But a huge number of QA teams are still doing manual testing — and that segment hasn't gotten much attention from AI tooling. Writing comprehensive test cases, covering edge cases, tracking validation across reviewers, linking failed tests to defects — all of that is still mostly manual, repetitive work. What I built Evaficy Smart Test covers the full manual QA workflow: Projects & Scenarios — organize test cases by project, with role-based access for Owners, POs, Tech Leads, and QA AI test case generation — generate comprehensive test cases including edge cases and negative scenarios based on test type and affected page Expert validation — PO/Tech Lead review workflow before test cases go live Test runs — step-by-step execution with real-time pass/fail tracking Defect tracking — inline defect logging with full traceability, one-click push to Jira AI Risk Insights — scores scenarios by failure risk using historical test run data, so teams know where to focus before a release Stack & technical notes Built with React and Material UI on the frontend, with Stripe for subscription billing. Some of the more interesting build challenges: Designing the AI generation flow to feel fast and controllable rather than a black box — letting users specify criteria test type, affected page, custom fields rather than just a free-text prompt Building the validation workflow so AI-generated and manually-written test cases go through the same review process, since trust in test cases matters more than where they came from Keeping the defect → Jira sync simple one click, no field mapping headaches since context-switching is the 1 complaint QA teams have about their current tools Try it out If you work in QA, or you're a dev/PM who's tired of testing falling through the cracks, I'd genuinely love your feedback. 🚀 We're live on Product Hunt today: Evaficy Smart Test - Launch Page https://www.producthunt.com/products/evaficy-smart-test?launch=evaficy-smart-test 🔗 Try Evaficy Smart Test: Evaficy Smart Test https://app.evaficy.com Happy to answer any questions about the build, the AI integration, or manual QA workflows in general — drop a comment below