QAEverest.ai is an AI platform for software testing (QA), with the goal of transforming user stories into runnable test suites and integrating multiple workflows such as API, Web UI, mobile, performance, security, accessibility, and exploratory testing into a single platform.
Purpose
According to the official description, QAEverest is used to transform user stories or scenarios into test cases and further automate API, Web UI, mobile, and performance testing. It extracts input from Jira, GitHub, Figma, or plain English descriptions, generating test cases and runnable test suites.
Main Capabilities
- Test case generation: transform user stories into test cases and generate runnable test suites.
- Self-healing tests: officially stated that test suites remain alive after application redesigns or redesigns, automatically repairing failed locators (self-healing locators).
- Time-travel replay: replay any UI test run frame by frame, with AI root cause analysis pointing out the exact failing step, screenshot, and network calls.
- Visual regression detection: based on pixel-level screenshot comparison and baseline management, detect layout regressions before users see them.
- Autonomous exploratory testing: after pointing to a URL, the AI can log in, crawl the application, generate Gherkin flows, execute in real time, and turn failures into Jira defect cards with one click.
- Test health and ROI dashboard: track self-healing locators, quarantined flaky tests, failure clustering, and engineering hours saved.
- Quality command center: provide a single "release confidence score" that combines functional pass rate, RTM coverage, flakiness, and CI release gates.
- AI Copilot assistant: ask questions in natural language, such as "Where are my coverage gaps?" or "Run my last failed suite," and Copilot will stream tool actions and return structured answers.
- Figma design import: import live Figma boards directly into the test generation process.
- Automated suite import: connect GitHub, GitLab, or Bitbucket repositories and transform existing Playwright, Cypress, Selenium, or pytest tests into reviewable, self-healing test cases; they will not run until you approve.
- Multi-platform TCM export: push test cases to TestRail, Zephyr Scale, Qase, QTest, PractiTest, or Azure Test Plans.
- Developer tool integration: provides an MCP server and a published JetBrains plugin, and mentions VS Code, placing generation and execution inside the IDE.
How to use
An online trial is officially provided, allowing users to paste a user story and observe the engine working without registration; it also supports input from Jira, GitHub, Figma, or plain English text. Users can import existing automation suites (which run only after approval) and, through the export feature, keep tests as readable, runnable, retainable code.
Use cases
The industries officially listed include financial services (payment flows, KYC, fraud checks), retail and e-commerce (omnichannel checkout, inventory synchronization, payment flows), healthcare (HIPAA-sensitive processes, EHR integration and accessibility), insurance (quote-to-underwrite, claims workflows and document generation), utilities and telecommunications (billing, provisioning and outage processes), and ERP and manufacturing (order-to-cash, inventory and supply chain integration).
Security and enterprise capabilities
Supports SAML 2.0 single sign-on (Okta, Azure AD, or Google Workspace, including just-in-time user provisioning), role-based access control (Viewer, Member, Lead, Admin, with project-level overrides), audit-ready activity logs (recording user, IP address, and timestamp), multi-tenant isolation, encrypted credentials (bcrypt-hashed passwords and session-signed tokens), and configurable security policies (session timeout, IP allowlist, password rules, account lockout thresholds).
Limitations
The official offering includes a free tier with no credit card required. However, details regarding specific pricing, the feature limits and quotas of each tier, the specific platform scope of mobile support, and the concurrency scale of performance testing are not yet explained in the provided materials.
