Tusk is an AI-powered testing platform designed to automatically transform production traffic into unit and API tests. Its primary objective is to prevent software regressions that are often overlooked by both automated agents and human reviewers, thereby significantly enhancing code coverage. By leveraging artificial intelligence, Tusk enables development teams to deploy code more rapidly and with greater confidence, ensuring that new changes do not introduce errors.

The platform generates verified test cases for pull requests by analyzing the codebase and business context. It seamlessly integrates into continuous integration pipelines to enforce test coverage requirements without interrupting the workflow of engineers. A key feature is the one-click push functionality, which allows developers to review AI-generated test cases before committing them to a branch or creating a separate PR for testing.

Tusk operates autonomously, self-iterating on tests when errors are detected during execution, which eliminates the need for manual intervention or back-and-forth with AI copilots. It also includes self-healing capabilities that automatically update existing test suites with each commit to maintain alignment with the latest business logic. Tests can be executed either in CI environments or locally, providing flexibility and ensuring that bugs are caught early in the development cycle.

The unique approach of Tusk involves using AI to mimic real-user behavior from production traffic, creating high-quality tests that cover thousands of edge cases in minutes. This method ensures comprehensive testing that traditional approaches might miss, leading to more robust software.

Benefits of using Tusk include early detection of regressions, efficient fixing of functional bugs, and substantial increases in code coverage. Use cases involve testing code changes in pull requests, maintaining test suites for large codebases, and supporting fast-paced development cycles without compromising quality.

Tusk is trusted by engineering leaders at fast-growing companies, as evidenced by testimonials from organizations like DeepLearning.AI, Hamming, and Promptfoo. It is ideally suited for software development teams seeking to improve their testing processes, particularly those managing extensive codebases or requiring reliable regression testing to build a quality-first engineering culture.