Aethosthe epoch-3 verdict

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.

THE QUESTIONS IT ANSWERS
1
What scale works for my use case?An advisor answers this from your data and budget alone, before anything runs — every size class shown with a reason.
2
Which models are right for me?Candidates at that scale, each with its status and reason — and short probe runs on your data to test them before you commit.
3
How do I get to a trained model without wasting compute?While it trains, the system reads how much the model still has to learn from your data, and recommends accordingly — continue, stop, or don’t start at all. Every call is graded afterward against what actually happened.
compute passed through at the provider’s rate · the judgment is what we charge for
lossthe planned budget123not spentepoch 3 · the verdict: stopwhat the rest of the budget would buycheckpoint saved · machine released · meter stopped

One judged run, drawn. The verdict can also be continue — or no call at all, when the evidence isn’t readable.

How it works

1
Your data, receipted.

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.

2
An advisor that shows its work.

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.

3
Probes before commitment.

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.

4
The verdict, three epochs in.

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.

5
Your model, with the record.

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. Twenty years of engineering: 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; you get the model, with the evidence for every call.

See it run

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

amrit@aethossystems.ai