Platform
Each module does one job, runs on the AI model you choose, and writes its output into the tools you already use. Start with test generation and add the rest when you're ready.
Requirements and design
Feed it Word, Excel, PDF or Jira. It extracts atomic requirements, scores how testable each one is and suggests rewrites, maps scenarios, writes prioritised test cases and a risk-based plan.
Test generation
Unit, integration, end-to-end and contract tests, generated from your code, your OpenAPI spec and a live inspection of your app. The platform checks every generated test refers to real symbols and real page elements before it reaches you.
Live UI and UX checks
An agent drives Chromium against your staging or production URL, takes a screenshot per step, diffs visuals against your last good run and checks WCAG on every page. Works behind login and SSO using a session you provide.
Bug detection
Reads the changed code the way a careful senior engineer would: null handling, off-by-one, race conditions, resource leaks, error handling, boundary conditions and more. Each finding names the file and line and proposes a patch.
Security and threat modelling
Static analysis, known-vulnerable dependencies and leaked credentials on every change. Live DAST against the running app. A STRIDE, LINDDUN or PASTA threat model where each threat is corroborated against actual findings rather than listed from a template.
Traceability and reporting
Requirement to scenario to case to test to code, every link resolved against your repository. Failure triage from your CI reports, performance verdicts from your k6, JMeter or Gatling results, and one dashboard per product across all its repos.
How results are labelled
The AI does the judgment. The platform verifies it: every cited file and line resolved, every selector checked, everything that can run, run. Then it labels the result so you know exactly how much to trust it.
It ran. The test executed, the scan completed, the link resolved. Use this number in a release gate.
We produced it but couldn't run it here, for instance no JVM in this stage. Review before trusting.
We tried and the result didn't settle: flaky run, timeout, ambiguous match. We tell you rather than guess.
Nothing to measure: no load report attached, no UI in this service. Shown as N/A, never as a pass.
Bring your own AI
Anthropic, OpenAI, Google, or any OpenAI-compatible endpoint including gateways in front of Bedrock or Azure. Pick a different model tier per module: a cheap fast one for classification, a strong one for test design. Every run records which model did the work.
llm: provider: anthropic # or openai, gemini, openai_compatible base_url: https://llm-gateway.internal modules: requirements_extraction: { enabled: true, tier: fast } test_generation: { enabled: true, tier: strong } bug_detection: { enabled: true, tier: strong } threat_model: { enabled: true, tier: strong } performance_analysis: { enabled: false } vigil: { enabled: false } # turn on when you ship AI features
Scope
This list is on purpose. It's how we stay good at the part in the middle.
Use BrowserStack, Sauce Labs, your own grid. We generate for them, we don't compete with them.
We execute what we generate. Existing automation keeps running where it runs today; we triage its failures from your CI reports.
We write into Jenkins, GitHub Actions, GitLab, Azure DevOps, Jira, Xray, Zephyr. We don't ask you to leave them.
Web, API and mobile (via Appium) are our ground. Desktop and TV aren't on the plan.
We shorten both. We don't sign off for either.
Next step
A demo runs on your code, not ours. Forty-five minutes, one service, real findings. If it's useful, we go into a four-week pilot inside your CI.