Train open-weight models — and know, while it’s running, whether the rest of the run is worth paying for.
Built around the questions teams actually run into when they want a model of their own.
At the heart of it: our own technology for judging a training run’s return on investment while it’s running — and using that to spend the compute budget well. Validated on our own runs. It shows you where it says no, not just where it says yes.
One judged run, drawn. The verdict can also be continue — or no call at all, when the evidence isn’t readable.
How it works
You upload a corpus and get a receipt: what’s in the file, counted, with a fingerprint. The receipt describes your data; it doesn’t score it.
Every candidate model appears with its status and a reason — recommended, not advised, or unable to run in your regime. The advisor advises. You decide.
Short probe runs test the candidates on your data — same budget, same judge. The result can be “don’t train from this slate yet.” That answer costs a fraction of a wasted run.
On the run you commission, the judgment reads the curve at epoch 3. Stop means the checkpoint is saved, the machine is released, and the meter stops. Continue means the data still has something to teach. When the evidence isn’t readable, it says so instead of guessing.
You get the weights and a report that grades every call against what actually happened — including the calls it got wrong. You never have to take the verdicts on trust.
About
Aethos is built by Amrit Kumar. In networking since 2007: Cisco, Arista, director of engineering at Apstra, then Juniper through the Apstra acquisition.
Aethos is intent-based training: you declare the data, the target, and the budget; the system decides how the budget gets spent, and shows the evidence for every call.
See it run
The demo is shown live, on real runs — including the ones where the answer was no.
Request the demo