Find the bugs that matter

Before your customers do.

Coding agents fix bugs you know about. LogicStar finds the ones you don't, ranks them by ARR impact, and surfaces only what matters.
Try against your repo

How it works

Most bugs that hurt revenue never reach a ticket. LogicStar surfaces them, ranks them by impact, and ships validated fixes automatically.

01 Your systems already contain the signals

01 Your systems already contain the signals

LogicStar turns them into a clear priority of what to fix next.
Which bugs affect customers, which ones threaten revenue, and what to fix next.

Application view of unresolved critical bugs waiting weeks compared with LogicStar’s autonomous resolution that clears them in hours

02 Rank by revenue & customer impact, not severity

A P1 in dead code doesn't matter. A P3 in your highest-revenue checkout flow does. LogicStar ranks every defect by ARR at risk and the customers it affects, so your team fixes what moves the business, not what sounds urgent.

Task list titled Fixing bugs from backlog showing five fixed issues including Cart button.ts and Incorrect font rendering, with a label stating 10 issues fixed.

03 Fix bugs before they become incidents

Bugs don't start as incidents. They start as warnings nobody had time to investigate. LogicStar cuts through the noise and proposes a validated fix.

What you're betting on.

Engineering teams on Logicstar

If something is in our backlog, it's because our engineering team did not think it was a quick fix. It would be a high benefit if Logicstar gave an automated fix.

Abstract stylized letter T logo composed of overlapping green and dark teal curved shapes on a black background.

Diego Amarillas

CommonRoom

On an average week probably like 70% of on-call work is coming from Datadog. If somebody's on call we don't expect them to have time to work on their actual issues

Lazar Kanelov

Localstack

It's good for us to have 10 issues or requests per week that we can check out. Maybe it found some problems or some optimizations that we should address.

Federico Gossi

Scalera

the correlation aspect where you're reading from like other sources as opposed to just the codebase. That is also like an awesome addition I would say

Sandeep Sripada

Frec

FOUNDING TEAM

Boris Paskalev
CEO

Serial entrepreneur, co-founder DeepCode (acq. by Snyk), EMBA (TRIUM), MSc (MIT).

Mark Müller
CTO

PhD from ETH Zurich, 15+ papers and 400+ citations. Notable industry collaborations.

Veselin Raychev
Chief Architect

Serial entrepreneur, top researcher, co-founder of DeepCode (acq. by Snyk), PhD (ETH Zurich).

Martin Vechev
Co-Founder and Advisor

Professor at ETH Zurich. 200+ publications in AI, networking, programming paradigms and others.

Backed by:

Built on research, not assumptions

Proven on real-world systems, we publish the leading benchmarks for AI coding agents. That same expertise drives our internal evaluations, so LogicStar keeps getting better as models evolve.

84%

validating tests generated

LogicStar reproduces every bug with a failing test that proves it's real and validates fixes actually resolve them. State-of-the-art performance on SWT-Bench Verified.

60%

overestimation of success rate in SWE-Bench Verified

Many AI coding agents overfit to a single benchmark. We automatically create new benchmarks for every use-case and show popular code agents lose up to 60% of performance on an application focused benchmark of 366 diverse codebases.

33%

of working AI-generated code is exploitable

Even frontier models produce exploitable backends. Across 392 tasks, one in three working solutions contains SQL injection, path traversal, or code injection vulnerabilities.

+20%

cost increase, zero performance gain

Over 60,000 repos include AGENTS.md files to guide AI agents. Our evaluation shows these files reduce success rates by up to 3% while adding 20% to inference costs.

63%

of AI refactoring attempts break code

AI agents solve only 22% of multi-file refactoring tasks and introduce breakage in 63% of attempts. CodeTaste measures whether AI restructures code the way a senior engineer would.

LogicStar AI logo – autonomous software maintenance and self-healing applications

Stop guessing what to fix

Start fixing what matters

LogicStar shows the bugs impacting customers and revenue, ranked and ready to act on.

No workflow changes. Results in ~1 hour.

Screenshot of LogicStar generating production-ready pull requests with 100 percent test coverage, static analysis, and regression validationScreenshot of LogicStar generating production-ready pull requests with 100 percent test coverage, static analysis, and regression validation