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Yalitest is a narrow bet: the bottleneck in QA is not only running tests. It is thinking up the right tests early enough for the team to do something with them.
Every product team eventually asks a tester to find problems in a shrinking release window. Yalitest gives that person more perspectives: requirements, risks, roles, edge cases, and blocked questions assembled into a plan that can be reviewed.
Tests, not transcripts.
Traceability, not trust-me output.
Flat pricing that lets the whole team review.
Good QA tools should make responsibility clearer, not hide it behind generated confidence.
Every generated test should explain which requirement, role, or risk caused it to exist.
AI drafts the first pass. QA, product, and engineering still approve what matters.
The product is for the person who has to defend release quality when the schedule gets tight.
Early users talk directly to the founders. The product is still shaped by real PRDs, real releases, and real QA reviews.
Co-founder focused on the way Yalitest thinks about QA judgment, edge cases, and reviewable output.
Co-founder focused on the agent pipeline, product workflow, and clean handoff into existing engineering systems.
Teams bring the messy documents that make the product honest: partial specs, screenshots, tickets, and release pressure.
The product keeps moving toward one simple promise: requirements in, reviewable QA coverage out.
The first prototype reads real PRDs and drafts plain-language cases.
Design partners push the product from chat output toward a reviewable workspace.
Yalitest focuses on traceable AI test case generation for web QA teams.
We answer every founder email, especially the specific ones.