Before your customers do.


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

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.

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.

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.
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.
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
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.
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








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.
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.
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.
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.
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.
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 shows the bugs impacting customers and revenue, ranked and ready to act on.
No workflow changes. Results in ~1 hour.

