> ## 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.

# Data

> Sourcing and structuring the behavioral data a model of human behavior needs.

<Warning>
  **Not self-serve.** Data work is scoped with the team before anything is built. [Reach out](mailto:jonathan@jeantechnologies.com) to start.
</Warning>

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.

## What we do here

<CardGroup cols={2}>
  <Card title="Source" icon="search">
    Find the behavioral data that actually carries signal for your task, inside your systems or through partners.
  </Card>

  <Card title="Structure" icon="table">
    Turn raw events into sequences: one actor, ordered actions, timestamps, outcomes. The shape a sequence model can train on.
  </Card>

  <Card title="Enrich" icon="wand-sparkles">
    Attach item content, taxonomy, and derived attributes so the same interaction carries semantics, not just an opaque ID.
  </Card>

  <Card title="Govern" icon="shield">
    Consent, retention, and scoping decided up front. Anything that leaves your boundary is agreed in writing first.
  </Card>
</CardGroup>

## What good behavioral data looks like

Three properties separate data that trains a useful model from data that does not.

| Property           | Why it matters                                                                                                                                                                                   |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Sequential**     | Order carries preference. A user who bought A then B is a different signal from one who bought B then A. Unordered aggregates lose most of it.                                                   |
| **Consequential**  | A click is weak. A purchase, a retained subscription, a second date, a completed hire: these are strong. Weak signals are cheap and abundant, strong signals are what the model is actually for. |
| **Content-linked** | Every interaction should resolve to an item with real content attached. Interactions over anonymous integer IDs cannot transfer to a new catalog or handle a cold-start item.                    |

<Tip>
  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.
</Tip>

## How an engagement runs

<Steps>
  <Step title="Scope" icon="compass">
    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.
  </Step>

  <Step title="Pipeline" icon="git-branch">
    We build the extraction and structuring pipeline against your systems. It runs on your infrastructure unless you decide otherwise.
  </Step>

  <Step title="Validate" icon="check">
    Held-out evaluation against your current production baseline, before any model training is committed to.
  </Step>

  <Step title="Hand off" icon="arrow-right">
    The structured dataset feeds [foundation models](/products/foundation-models) or a [semantic ID tokenizer](/products/semantic-ids).
  </Step>
</Steps>

<Card title="Talk to the team" icon="mail" href="mailto:jonathan@jeantechnologies.com" horizontal>
  [jonathan@jeantechnologies.com](mailto:jonathan@jeantechnologies.com)
</Card>