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

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
🔗 Try Evaficy Smart Test: Evaficy Smart Test

Happy to answer any questions about the build, the AI integration, or manual QA workflows in general — drop a comment below!