> ## Documentation Index > Fetch the complete documentation index at: https://docs.jeanmemory.com/llms.txt > Use this file to discover all available pages before exploring further. # Foundation models > Foundation models of human behavior. First release: a foundation model for recommendation. **Closed beta.** Weights and inference access are granted per engagement, not self-serve. [Reach out](mailto:jonathan@jeantechnologies.com) to scope a pilot. Language models are foundation models of text. We are building the equivalent for behavior: models pretrained on large volumes of real human interaction sequences, which transfer to a new product or catalog with little task-specific data. The bet is the same one that worked for language. A model trained broadly enough on how people act stops needing a bespoke architecture per application, and a downstream team gets a strong starting point on day one instead of after a year of collecting their own labels. ## First release: recommendation Recommendation is where behavioral data is densest and where the evaluation is least ambiguous, so it is where we started. The model is a sequence model over user interaction histories. It consumes a user's ordered actions and predicts what comes next, in the same autoregressive way a language model predicts the next token. Items are represented as [semantic IDs](/products/semantic-ids) rather than as rows in an embedding table, which is what lets the model transfer across catalogs at all. A new item is representable the moment it has content. No interaction history required before it can be recommended. Pretrained on interaction data across domains, then adapted to your catalog. Useful before you have accumulated your own signal. The model generates an item identifier directly. Constrained beam search over the codebook replaces approximate nearest neighbor over an index. Parameter count is set by the codebook, not by the number of items. A catalog can grow without the model growing with it. ## How you would use it Fit a semantic ID tokenizer on your item content. See [Semantic IDs](/products/semantic-ids). Supervised fine-tuning on your user histories, expressed as next-token prediction over semantic IDs. A reinforcement learning pass against your actual ranking objective, not a proxy. What counts as a win is your definition, set at onboarding. Generate recommendations through the API, or take the weights and serve them yourself. ## Beyond recommendation Recommendation is the first head, not the thesis. The same pretrained behavioral backbone is what the other [downstream tasks](/products/simulation) run on: matching, personalization, memory, and simulation of populations. We are deliberately not shipping those as products yet. They come after the backbone is good enough that they are adaptations rather than separate systems. ## Status and access | | | | ---------------- | ------------------------------------------------------------------------------------ | | **Stage** | Closed beta | | **Available** | Recommendation head, per engagement | | **Deployment** | Hosted API, or weights in your environment | | **Prerequisite** | A structured interaction dataset. We can help build one, see [Data](/products/data). | [jonathan@jeantechnologies.com](mailto:jonathan@jeantechnologies.com)