Train open-weight models — and know, while it’s running, whether the rest of the run is worth paying for.
You bring your data, and we hand back a trained model that fits the way your business works, running in your own cloud and ready to use.
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.
It runs in your cloud
Aethos runs inside your own cloud account. Your data, every training run, and the finished model stay there, and the model can be served from an endpoint in the same account. Nothing leaves your network.
Who it is for
Teams without machine learning staff get a finished model they could not build themselves. Teams that train their own models get back the compute and the time that wasted runs take, so the next model starts sooner.
A typical first project is a model that sorts or routes a company’s own records, such as sending support tickets to the right team, trained on that company’s history.
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. Every candidate’s results are shown side by side, and you choose which one to train fully. The result can also 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. A call outside what the system has been validated on is marked advisory, and an advisory call never stops a run on its own.
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. A finished run can also be trained further on its original plan, or used as the starting point for a sweep.
About
Aethos is built by Amrit Kumar. Before Aethos he spent about twenty years building networking infrastructure, including Arista Networks in its early days and Apstra, where he joined early and stayed through its acquisition by Juniper. Most recently he led a global engineering team at HPE.
You declare the data, the target, and the budget. The system decides how the budget gets spent. You get the model, with the evidence for every call.
See it run
We are looking for our first design partners. The demo is shown live and runs the service end to end: data goes in, the run is judged while it trains, and a model comes out with its graded report.
Request the demo