MODELS · OFFERING 03
Atlas Hosted Training
We turn the work you already do into RL environments. No GPUs to buy, no ML team to hire, and your data never leaves your network.
What you send
Examples of the job done well: documents, tickets, call transcripts, past decisions. If you are already running agents with us, your live traffic counts too.
What we do
Our engineers turn those examples into training tasks and train an open model on them. You never touch a GPU, a cluster or a training script. Weeks, not a research project.
What counts as good
You decide what a right answer looks like before any training starts. We write the scoring with your team, and you can re-run the tests yourself whenever you want to check our work.
Where it runs
Each training run gets its own machine, created for that run and destroyed when it ends. Nothing carries over between runs, or between customers.
Proof
Every example and every score is logged through Atlas Sylo. When compliance asks where the training data came from, you have an answer with receipts.
What you get back
An endpoint. A smaller model tuned to your job, which costs a fraction per call of a frontier API and beats it at that job. The weights stay yours.
How it is delivered
We host it by default. If your data cannot leave your network, the same setup runs inside your own cloud instead, and nothing is rebuilt.
$ curl https://api.atlas.internal/v1/chat/completions -d '{"model": "your-collections-agent"}'
It gets better the longer you run it.
Once your model is live, the work it does becomes the next round of training data. We look at where it got things wrong, turn those cases into practice, and train again. Each round it gets cheaper and more reliable, and it drifts further from anything a competitor can buy off the shelf.
Step 01
Send
You hand over examples of the work. If your agents are already running, we start from what they do.
Step 02
Shape
We turn those into training tasks, and agree with you how the model will be scored.
Step 03
Train
We run the training on our own machines and benchmark the result against a frontier model on your job.
Step 04
Deploy
It ships behind an endpoint. Your existing code keeps working, and we measure it on live traffic.
Weeks, not research projects
Isolated per run
Weights stay yours
The best model for your job is the one trained on it.
Stop waiting for the next frontier release to fix your problem. Book a demo and we will walk through the work you already do, and what a model trained on it would be worth.
6-9 wks
To production
7 offerings
One stack
0
Customer data leaves your network