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Not self-serve. Data work is scoped with the team before anything is built. Reach out to start.
Foundation models of human behavior are bottlenecked on data, not architecture. The public corpus is full of what people said. It is very thin on what people did, over time, with consequences attached. Most of that signal already exists. It sits in event streams, order histories, session logs, and CRM tables inside companies that never built it into a shape a model can learn from.

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What we do here

Source

Find the behavioral data that actually carries signal for your task, inside your systems or through partners.

Structure

Turn raw events into sequences: one actor, ordered actions, timestamps, outcomes. The shape a sequence model can train on.

Enrich

Attach item content, taxonomy, and derived attributes so the same interaction carries semantics, not just an opaque ID.

Govern

Consent, retention, and scoping decided up front. Anything that leaves your boundary is agreed in writing first.

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What good behavioral data looks like

Three properties separate data that trains a useful model from data that does not.
If you only have one of the three, that is still worth a conversation. The most common starting point is a large sequential log with no content linkage, and the fix is usually a catalog join rather than new collection.

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How an engagement runs

Scope

We look at what you have and what you want the model to predict. Output is a written spec of the data, the target, and the evaluation.

Pipeline

We build the extraction and structuring pipeline against your systems. It runs on your infrastructure unless you decide otherwise.

Validate

Held-out evaluation against your current production baseline, before any model training is committed to.

Hand off

The structured dataset feeds foundation models or a semantic ID tokenizer.

Talk to the team

jonathan@jeantechnologies.com