LogicStar

Transforming Software Development with AI

At LogicStar, we are transforming software development and maintenance through the power of agentic artificial intelligence. Our mission is to empower engineering teams with an innovative AI agent that autonomously resolves software bugs, enabling developers to focus on creativity and innovation.
Founded by a team of leading software engineers, researchers, and AI experts, LogicStar was established to enhance productivity and improve software maintenance in an increasingly complex landscape that LLMs alone cannot comprehend. Our solution seamlessly integrates into existing workflows, effectively handling software issues of any type and format. With real-time implementation of fixes that rival the accuracy of senior software engineers, we help teams achieve greater efficiency and innovation.

Accelerated Development Cycles
Enhanced Application Quality
Increased Team Productivity
Reduced Technical Debt

Verification of AI-Generated Code

LogicStar harnesses AI along with classical computer science to empower engineering teams and AI agents to autonomously maintain commercial applications, enabling faster cycles and improved application quality. Founded by top experts, we are dedicated to advancing the future of software.

Fusing AI with Proven CS Methods

AI Agent: Combines classical computer science methods with LLMs to deeply analyze the entire application: from code to cloud. Creating a rich and structured application representation for AI consumption.

Application Mocks Tailored for Agentic AI

Advanced application mocks are generated for each issue that the LogicStar agent reviews, capturing all relevant application components to ensure accurate reproduction of the problem.

AI-Powered Mock Execution Environment for Bug Reproduction

The AI Mock Execution Environment is designed to autonomously reproduce application bugs and generate failing tests, ensuring every issue is accurately captured and ready for resolution.

Evaluate and Verify AI Fix Suggestions at Scale

In this final step, our agent systematically evaluates AI-generated fix suggestions, leveraging structured context about the application, problem, and reproduction steps. The result: one or more verified fixes, ready for seamless manual code review and deployment.

What Experts Are Saying.

LogicStar doesn't even need to show how you can fix a bug. Just pinpointing the exact location of the bug already saves a lot of time.

Ori D.

If I have a bug, I'd love to have an agent that finds the right files, tries to reproduce the bug, it fixes the bug and then it verifies that it's correct so I can review it.

Arvid L.

Engineers often face frustration navigating the multiple combinations of inputs needed to reproduce an issue. I appreciate how LogicStar proposes to simplify the challenging task of reproducing issues, saving valuable time in MTTR.

Sam R.

...having 20 things that we can’t fix automatically is a bunch of noise, having 1 proposed solution that appears to fix something is much more interesting