The brief
Cernova is the maintenance layer.
01 — The world
From businesses to solo developers, everyone is shipping AI agents and workflows. Tools like Cursor and Replit made building these projects completable in a weekend.
02 — The problem
Silent failures. Green dashboards. Users as the alarm.
Current monitoring tools demand high technical depth, enterprise budgets, and time no small team has. And AI usually fails silently — the first person to notice is usually a user in production.
03 — The fix
One line of code — or just tell your coding agent to install it — and our own ML watches every call your agents make. It learns what normal looks like for every step of your pipeline. When something breaks the pattern, you don't get a dashboard to stare at. You get what broke, why, and the fix.
04 — Who, now
Today we're building for indie developers and small teams shipping agents without an ML team behind them. People who can stand up a workflow in a weekend — and still need something watching it on Monday.
05 — Who, later
As those teams grow — and for companies already running serious agent fleets — the same detection becomes the reliability layer for production AI. We've already had those conversations. The demand is there.
06 — Future scale
The long arc is simple: the same engine that watches a weekend prototype becomes the reliability layer for production AI fleets — learning baselines across every step, catching silent regressions, and pushing what broke, why, and the fix to wherever teams already look.
“Anyone can build an AI system now. Keeping it working is the unsolved problem.”
One line of code. Or tell your coding agent to install it. Free to start.