AI systems laboratory
We study the problems generic tools leave unresolved, then build models that understand one job deeply.
Most tools tell you something broke. Ours help explain why.
Our products
We choose a hard operational problem and stay with it until the work becomes simpler for the people doing it.
A local DevOps agent for small engineering teams. Lear reads your real infrastructure signals, finds what actually broke, and proposes a fix that waits for your approval.
Visit Lear →Prash was our first model for CI and CD failures. It taught us how much context a narrow problem demands, and became the foundation for Lear.
Explore Prash →How we build
The work starts with a real pain point, not a broad category and a polished demo.
Talk to the people carrying the operational load and study the failure in context.
Design the reasoning, data, and evaluations around one job.
Put it in front of the people who need it and learn from what happens.
What we believe
Trust is designed into the way our models observe, decide, and act.
People stay in control of consequential changes.
A suggestion is not a solution until the system confirms it.
Local operation keeps sensitive context where it belongs.
When the evidence is incomplete, the model says so.
Tell us what the system is doing, what you have tried, and where the usual tools stop helping.