Every company will own its models.
Supermodels turns your company's data into small, purpose-built AI models that you own. Lower cost, lower latency, full control, and no dependence on someone else's roadmap.
The first era of AI was a handful of giant models, rented by everyone. The next will be millions of smaller ones, each owned by the company it serves.
General-purpose models are remarkable, but most of what a business asks of AI is narrow, repetitive, and specific to that business. For that work, a small model trained on your own data can match or outperform a frontier model at a fraction of the cost and latency.
And unlike an API, it's an asset. It compounds with every new piece of data, runs where you choose, and stays under your control.
For the people accountable for AI in production.
Heads of AI, platform leads, and CTOs who own the bill, the risk, and the results.
API spend that grows with every new use case.
Move high-volume workflows onto models sized for the job, and watch cost per task fall as usage grows.
Behavior that shifts when a vendor updates its model.
Your model changes only when you retrain it. Every version is pinned, tested, and reproducible.
Sensitive data leaving your environment.
Train and serve inside your own cloud. Your data never trains anyone else's model.
No clear way to prove which model is right for the job.
Every model ships with an evaluation report against frontier APIs, measured on your own data.
From a description to a model in production. No ML team required.
Describe the job
Explain the workflow in plain language. We turn it into a precise task and an evaluation set.
Connect your data
Link the systems where the work already happens. We prepare it for training.
Train and evaluate
Your model is trained in days, then measured against frontier models on your own test set.
Deploy and improve
Swap a single endpoint. The model keeps retraining as new data arrives.
Your scoreboard
IllustrativeEvery model answers the same test set, drawn from your data.
| Model | Accuracy | Speed (p50) | Cost per 1,000 calls |
|---|---|---|---|
| Your current frontier model | 94.2% | 820 ms | 100 |
| Your provider's cheaper tier | 90.2% | 410 ms | 38 |
| A prebuilt open-weight model | 88.1% | 240 ms | 18 |
| Your Supermodel | 94.5% | 190 ms | 29 |
Illustrative numbers, not results. Cost is an index where your current model is 100. Calls the Supermodel is unsure about go to your current model.
How long does a model take to build?
Most models are ready in days. Larger or more complex workflows can take a few weeks, and you can follow progress throughout.
Do we need machine learning engineers?
No. You describe the job and connect your data. Preparation, training, evaluation, and hosting are handled for you.
How do we know it outperforms frontier models?
Every model is evaluated against leading frontier APIs on a test set drawn from your own data. You see the results side by side before switching any traffic.
Where does our data go?
It is used only to build and improve your models. For stricter requirements, training and hosting can run inside your own cloud.
