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.
