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Spec-only preview. Nothing described here is live. These docs are partly how we are working out what the product should be, so if a page is wrong about your problem, that is useful to us. Tell us.
Language models are foundation models of text. We are building the equivalent for behavior: models pretrained on what people actually did, which transfer to a new product or catalog without a year of collecting your own labels first. Four products, in the order they depend on each other.

1. Data

Sourcing and structuring the behavioral data a model of human behavior needs. Scoped with the team.

2. Foundation models

Models of human behavior. First release is a foundation model for recommendation. Closed beta.

3. Semantic IDs

Turn a catalog into discrete semantic tokens a generative recommender can generate. Spec preview.

4. Simulation

Rehearse a decision against a simulated population before it meets the real one. Research direction.

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The shape of the argument

Behavior is the missing corpus

The public corpus is full of what people said and thin on what people did. The sequential, consequential, content-linked data that a behavioral model needs mostly sits unused inside companies. That is product one.

Pretrain on it

Train broadly enough on real interaction sequences and you get transfer, the same way you do for language. A downstream team starts strong instead of starting from zero. That is product two.

Items need a vocabulary

A sequence model over items only transfers if items have meaningful identifiers. Random integer IDs do not transfer and cannot cold start. Semantic IDs can. That is product three, and it is the most concrete surface here.

Then run it forward

A model that predicts what a person does next can be run against a population to rehearse a decision. That is product four, and it is the least built.

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Where to start reading

Semantic IDs

The most developed product. Codebooks, collaborative signal, and a full API sketch.

API Reference

Every endpoint with a live schema.

Concepts

The matching data model. Users, schemas, matches, feedback.

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Get access

Nothing here is self-serve yet. Every engagement starts with a conversation about your data and what you want the model to predict.

Talk to the team

jonathan@jeantechnologies.com