The AI co-pilot for quality engineering.
A quality-tuned model grounded in your codebase, test suites, and ticket history. Built to ship — not to demo.
- Generates test cases from user stories
- Self-heals broken locators
- Explains failures in plain English
- Suggests missing edge cases
- Grounded in your private data
- Available via MCP protocol
Designed to fit how modern teams actually work.
Generate from requirements
Paste a user story or paste a Jira link. Get a full test suite — happy path, edge cases, and negative paths — in seconds.
Self-healing automation
When the frontend shifts, locators repair themselves. The model learns from your application's DOM and behavior.
Ask anything, in plain English
Why did checkout fail in EU? Which tests changed this sprint? The assistant has the context to answer.
Coverage gap analysis
Point it at a service or release; it tells you what's under-tested before you ship, not after.
Grounded & private
Your data trains nothing outside your tenant. Available on-prem, in your VPC, or single-tenant SaaS.
Open MCP server
Built on the Model Context Protocol — plug the assistant into Claude, Cursor, or any MCP-compatible client.
AI that earns trust.
- Quality-tuned LLM (not generic)
- Retrieval grounded in your repo
- Per-tenant data isolation
- BYO key (Azure OpenAI / OpenAI)
- Explainable outputs (cited sources)
- Built-in evaluations on each release
- Cost controls + per-team quotas
- Audit log for every generation
Other parts of the platform
See SimplifyQA on your own workflows.
30-minute live walkthrough with a solutions engineer. Bring your real tools, real data, real edge cases.