LogicStar

Transforming Enterprise Software Maintenance with Autonomous AI

At LogicStar, our mission is to reclaim developer time and improve software reliability by automating the most time-consuming part of engineering: maintenance. We deploy an ecosystem of AI agents and tools that collaborate, recursively call one another, and harness cutting-edge AI models to investigate, reproduce, and resolve your application issues. Our vision is a future where software problems are autonomously fixed before anyone even notices them.
Founded by a team of leading technologists, researchers, and AI experts, LogicStar enhances productivity and improves software maintenance in an increasingly complex landscape that LLMs alone cannot comprehend.

Reduces Mean Time to Resolution (MTTR)
Reclaims Developer Time for Innovation
Fixes Issues Across Your Entire Signal Chain
Delivers Verified, Production-Ready Fixes

Verification of AI-Generated Code

The LogicStar platform eliminates the high failure rate typical of AI coding tools when working on enterprise applications. Most AI tools generate solutions that require extensive human validation, creating more maintenance work rather than reducing it. Our system combines classical computer science methods for deep semantic code analysis with the latest LLM capabilities to automatically filter out hallucinations and unreliable fixes.
Unlike competitors that flood engineers with multiple suggestions — forcing teams to spend precious time reviewing, testing, and identifying valid solutions among many hallucinations — LogicStar delivers only production-ready fixes that have been systematically verified. Our proprietary verification pipeline ensures each solution meets the same standards as those from your senior engineers without requiring any oversight from your team.

1. Fusing AI with Proven CS Methods

Our Agentic Maintenance Engineer: deploys a workforce of specialized AI Agents that combine classical computer science methods with LLMs to deeply analyze your entire application: from code to cloud. These agents collaborate seamlessly to create a rich and structured application representation, enabling comprehensive analysis and resolution capabilities far beyond what single-agent systems can achieve.

2. Localization With Application Mocks

Advanced application mocks are generated for each issue that the LogicStar agents review, capturing all relevant application components to ensure accurate localization of the problem and all execution paths that can trigger the issue.

3. Bug Reproduction in Our Mock Execution Environment

The AI Mock Execution Environment is designed to autonomously reproduce application bugs and generate failing tests, ensuring every issue is accurately captured, can be reliably reproduced and is ready for validation of fixes.

4. Evaluate and Verify AI Fix Suggestions at Scale

In this final step, our agents systematically evaluate many AI-generated fix suggestions, leveraging structured context about the application, the bug, and reproduction steps. The result: one or more verified fixes with associated unit tests, ready for a quick 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 don’t know which 30% of the AI generated suggestions actually work, I still need to test 100% of them which is like reading other people's code all the time and no one likes that. LogicStar removes the wrong AI suggestions for me.

Nils W.

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