Try it live — no signup
This is the same live AI engine our customers run — not a canned demo. Refine a story or generate test cases, free, right here.
AI test automation, end to end
QAEverest turns a user story into a running suite — then keeps that suite alive through redesigns, and tells you exactly why a run failed instead of handing you a screenshot and a stack trace.
Free tier available · No credit card required
Counted from the product as it ships today.
Constant innovation keeps your QA ahead of the curve. Every capability below is available today — end-to-end, on one platform.
Scrub frame-by-frame through any UI test run. AI root-cause analysis surfaces the exact failed step, screenshot, and network call.
Quantify automation value: healed locators, quarantined flaky tests, failure clusters, and engineer-hours saved — metrics your team and stakeholders both need.
Point at a URL and walk away. AI logs in, crawls up to 25 pages, writes and runs a few Gherkin flows, and drafts a bug card for each failure that you can file to Jira or ClickUp in one click.
Pixel-level screenshot comparison with baseline management. Catch layout regressions across builds before users ever see them.
Push test cases to TestRail, Zephyr Scale, Qase, QTest, PractiTest, or Azure Test Plans in one click — no copy-paste, no re-entry, no sync drift.
One ship-confidence score — functional pass-rates, RTM coverage, flakiness, and CI release gates — so release decisions are data-driven, not gut-driven.
Ask "Where are my coverage gaps?" or "Run my last failed suite" in plain English. Copilot streams live tool actions and replies with structured answers.
Import live Figma frames directly into test generation. Design specs become test coverage automatically — no manual transcription, no drift.
Connect a GitHub, GitLab or Bitbucket repo and turn your existing Playwright, Cypress, Selenium or pytest tests into reviewable, self-healing test cases — folders mirror your repo, page objects are flattened, nothing runs until you approve it.
Test your own chatbot or AI agent through the endpoint it already has — answers, refusals, tool calls and personal-data probes, plus a red team of up to 60 attacks. A ready CI step can fail the build on the verdict.
Record a journey with the free FindMyLocator Chrome extension or Chrome's Recorder. QAEverest turns it into plain-English UI steps, recorded locators kept and passwords moved into test data, for you to review and save.
No tests yet, only code? QAEverest reads your routes, services, models and pages, ranks the features it finds, and writes reviewable test cases for the ones you tick — each with a confidence score and its assumptions spelled out.
What sets QAEverest apart
Test generation, cross-browser grids and visual diffing are table stakes now — every platform has them, including this one. These six are where the work actually went.
When the application changes underneath a test, the run re-anchors the broken step and keeps the original assertion rather than handing you a red build to triage by hand.
Passing checks are judged again against a slightly wrong expected value: after each run for new and changed test cases, or for the whole suite on demand. A good check now fails; one that still passes is flagged with a hint. A web UI test whose checks couldn't be confirmed is counted as skipped and marked “Not verified”, not passed.
Console, network and DOM state are captured across the whole run, so a failure opens on the exact step that caused it instead of a screenshot of the end state and a guess.
Every requirement mapped to the tests that verify it and — more usefully — the ones nothing covers at all, ranked by business risk rather than reported as a percentage.
The PR bot reads each pull request on GitHub, GitLab or Bitbucket, scores its risk out of 10 and names the tests worth running for that change. Requirements it touches that nothing tests are named too, and can be drafted into test cases for your review.
Extensions on the VS Code and JetBrains marketplaces, an MCP server for Cursor, Windsurf and other MCP editors, and a Claude Code plugin put generation and runs inside the editor your developers already have open.
The problem we set out to solve
Most tools fix a quarter of the problem — and the other three quietly absorb whatever time the first one saved. QAEverest was built to close all four.
“Writing test cases from a story takes days — and the story has changed by the time we finish.”
Tests generated from user stories and real production journeys — across UI, API, mobile, security and performance, in one place.
“A button moved and forty tests went red. None of them found a bug.”
Suites that re-crawl the app, detect drift and repair their own locators — flaky tests quarantined, failures clustered by root cause.
“A test failed overnight. All we have is a screenshot and a stack trace — go reproduce it yourself.”
Console and network captured for every step, replayable frame by frame, with AI reading the run and explaining in plain language why it broke.
“The release call comes down to a green tick and somebody's gut feeling.”
Functional results, coverage and stability fused into one ship-confidence score that can gate a release — every requirement traced, the gaps surfaced.
And all of it where the work already happens — VS Code, JetBrains, Jira, GitHub or a public API. A tool that solves a real problem in a place nobody goes hasn’t solved the problem.
Fits what you already run
Export to code you can read, run and keep. If you leave us, the tests keep working — that’s the point.
From FinTech to the factory floor — QAEverest.ai hardens the flows your business runs on.
Ship faster without breaking trust. Validate payment rails, KYC and fraud checks end to end — with audit-ready evidence on every release.
Release decisions on one page. Short go/no-go reports your QA team can hand straight to managers.
See the reports →From SSO to audit trails, QAEverest.ai gives security teams the controls they expect.
Bring your own identity provider — Okta, Azure AD or Google Workspace — with just-in-time user provisioning.
Five ready-made roles — Admin, Lead, Member, Viewer and UAT Tester — plus custom roles. Who may generate, run, scan, delete and sign off is checked on the server.
Every change and sign-in is captured with the user, IP address and timestamp for full accountability.
Every suite, run, test case, report and upload is checked on the server against the signed-in user before it is read, run or changed.
Passwords are hashed with bcrypt and sessions are protected with signed, server-stored tokens.
Admins set the session timeout, and how many failed sign-ins lock an account and for how long, for members signing in through the organisation's sign-in page or SSO.
Tokens saved for Jira, GitHub, GitLab, Bitbucket, Jenkins, Slack and other integrations never reach members' browsers — they only ever see a mask.
Before a generation, automation or copilot prompt reaches the AI model, recognisable keys, tokens and labelled passwords are swapped for placeholders.
A run report shared from its results page with someone outside your organisation leaves out secrets, screenshots and video links unless an admin has approved the recipient.
Per-organisation access control · Encrypted credentials · Full audit trail