ratl.ai
ratl.ai provides an AI-agentic testing platform where teams describe tests in plain language and autonomous agents build, execute, and maintain self-healing test suites. The platform handles visual regression detection, intelligent failure triage, and CI/CD integration, with a managed expert-led QA option for teams seeking additional support. It is trusted by organizations like Reliance Jio for e-commerce reliability.
- Developer Tools
- AI Agents
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Software testing remains a time-intensive bottleneck in development cycles, with teams often relying on brittle, manually maintained test scripts that break frequently and fail to keep pace with rapid iteration. This slows release velocity, increases engineering overhead, and leaves critical regressions undetected until after deployment.
Solution
ratl.ai offers an AI-agentic testing workspace where autonomous agents build, execute, and maintain test suites with minimal human intervention. Users describe test scenarios in plain language, and the platform's agents translate those instructions into robust, self-healing test cases that adapt to UI changes automatically. The workspace provides real-time execution insights, visual regression detection, and intelligent failure triage, allowing teams to focus on product quality rather than test upkeep. By embedding directly into modern development workflows, ratl.ai continuously validates new code and surfaces actionable feedback before production releases. The platform also offers a managed service where expert-led QA teams use AI-agentic workflows on behalf of clients.
Target Audience
Primary customers are software engineering teams, QA engineers, product managers, and DevOps professionals at product-driven technology companies, including e-commerce and retail organizations, who need to accelerate release cycles without sacrificing test coverage or reliability.
Features
- Natural language test authoring that converts plain-English instructions into executable test scripts
- Self-healing test automation that automatically updates selectors and flows when the application UI changes
- AI-driven failure analysis that groups root causes and suggests fixes to reduce debugging time
- Visual regression testing with pixel-level diffing to catch unintended UI changes
- Seamless integration with CI/CD tools like GitHub Actions, Jenkins, and GitLab for automated test execution on every commit
- Parallel test execution across cloud-based device and browser matrices to shorten feedback loops
- Analytics event validation to confirm tracking events like "product searched" fire with correct payloads