We host a small model for your routine model calls and send only the ones it is likely to get wrong to a frontier model. Those hard cases become its training data, so it takes over more of the work every month.
No fine-tuning data needed to start. We host an open model sized for your task and put it in front of your traffic.
This is what we build. At every step it judges whether the small model can be trusted with this one, the way people notice when their first answer might be wrong. When it can't, and a mistake would cost more than a frontier call, the step goes to the frontier model. How it works.
Every step the frontier model had to handle is a labelled example of exactly what the small model gets wrong. We train on those, so it takes over more each month and your bill keeps falling.
Every deployment teaches the metacognition layer where fast models fail and what fixes them. Our goal is a foundation model for metacognition: one general-purpose model that knows when a fast model can be trusted, on tasks it has never seen, so every new deployment starts from it instead of from scratch. Our tech.
Pick what you use language models for and enter your monthly bill.
A rough estimate, not a quote. Assumed: the small model costs one twentieth of a frontier call, including hosting on your own servers, and it takes the share of calls shown for each use once it has learned from your hard cases. Early on it takes less. Our fee is 20% of the savings, so you only pay if you save.
A share of what you save, plus a setup fee for each deployment. If the bill doesn't go down, you don't pay the share.
Model providers could add routing. But the hard part is not the switch. Real deployments always bring cases no model has seen: your products, your policies, your customers. Finding them, writing the checks and training on them takes hands-on work at each customer. That work, and the data it produces, is what we sell. A provider that earns money per token also has little reason to cut your token bill.