We test with AI, so you don't have to
We built an AI-driven QA system that tests without human intervention. We can build the same for your project.
Why the traditional approach fails
Brittle tests
One button-style change and half the test suite breaks. Before every release we spend hours fixing selectors instead of testing.
It doesn't scale
Developers ship features faster than anyone could write tests for them. The backlog only grows.
Blind spots remain
You only find what you thought of. Unexpected bugs, unusual user journeys and edge cases stay invisible until they surface in production.
That is why RAITEST is different: it doesn't run scripts, it thinks like an experienced tester, and it learns from its mistakes.
How it works: five steps, zero manual work
Understand: it reads the task
It reads the Jira ticket and the acceptance criteria, and checks which similar features it has tested before. It looks up in its knowledge base (RAG) what worked and what didn't, so it never starts from scratch.
Explore: it walks through the application
In half a minute it walks the whole interface with a Playwright browser: menus, forms, buttons, login flows. It doesn't guess: it actually sees the DOM and understands the structure.
Plan: it builds the test plan
From the ticket requirements and the explored interface it assembles test cases. For each one it defines what to check, exactly where, and what counts as a pass.
Execute: it actually clicks through
It clicks, fills in and navigates in a real browser, just like a person would. It tests APIs too. If a button or field has changed, a three-level self-healing mechanism looks for the element before giving up.
Report: it reports with evidence
Every test case comes with a screenshot, a detailed result and a confidence score. It doesn't just say “failed”: it explains why and classifies the severity of the bug.
What makes it different?
Self-healing tests
If the interface changes and an element disappears, the system tries three different ways to find it: similarity search in the page structure, visual recognition from the screenshot, and if needed, a full re-crawl of the page.
Handles several portals at once
In a single run it goes through the admin interface, the customer portal and the partner interface. It handles the login for each separately, with no per-portal configuration.
Learns from previous runs
What worked once, it remembers and reuses next time. It also remembers what could not be tested, so it doesn't waste time on things it already knows won't work.
Runs where it suits you
It can be installed on your own server with local AI models, run from the cloud (OpenRouter, Claude SDK), or mixed: tests on-premise, AI in the cloud.
Measured on a live project
It fits your infrastructure
On your own server
All data and models stay inside your company network. Full control, full security, which matters most in regulated industries (finance, healthcare, public sector).
In the cloud
OpenRouter or Claude SDK integration, instant scaling. No server costs, and the latest AI models are always available.
Hybrid
Tests and data run on your own server, while the AI reasoning comes from the cloud. The best of both worlds.
Want this for your own project?
We built RAITEST for ourselves, but we are happy to bring the technology and know-how behind it to your system. We design and deliver the AI testing system that fits your application, industry and infrastructure.
Book a meeting!Frequently asked questions
How is this different from Selenium or Cypress?
Selenium and Cypress run pre-written scripts that someone had to write and has to maintain. RAITEST generates the tests from Jira tickets and then runs them itself. When the interface changes, a traditional test breaks, while RAITEST tries to fix itself. In short: they are strong at reliably repeating known cases, we are strong at adapting and finding unknown bugs.
Which applications does it work with?
Practically anything reachable through a browser or an API: web applications, SaaS platforms, admin interfaces, multi-step workflows, mobile web. What it can't handle: native desktop applications and low-level, hardware-near testing (e.g. IoT firmware).
How secure is the on-premise installation?
Fully. The system runs in a Docker container on your server: not a single byte of data leaves your internal network. The model and every dependency live locally. Security audit, source-code review and compliance documentation are available. We also sign a custom NDA and contract if needed.
How does it fit into our existing workflows?
It starts automatically via a Jira webhook when a new ticket or sprint arrives. It notifies you on Slack and can be triggered from GitHub/GitLab CI/CD pipelines. You get the reports in XML, JSON or HTML. If you use TestRail or qTest, we can connect those too.
