> ## Documentation Index
> Fetch the complete documentation index at: https://docs.avra.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Relational Foundation Model

> A customer-specific relational representation layer pre-trained with self-supervised objectives on your schema, temporal history, and entity relationships.

The Graph Foundation Model is pre-trained on the economy. The **Relational Foundation Model (RFM)** is your customer-specific relational representation layer.

Your business is its own graph. Customers, accounts, products, transactions, support tickets, sessions — these are connected entities with their own structure. A model trained on public economic data cannot see that structure. A model trained on your data alone cannot see the world around it.

The RFM closes both gaps. It is pre-trained with self-supervised objectives on your schema, temporal history, and entity relationships, then composed with the GFM for downstream prediction tasks. Every downstream model inherits both: the broader economy from the GFM and your business context from the RFM.

## What it is

The RFM is a customer-specific relational representation layer: pre-trained with self-supervised objectives on your schema, temporal history, and entity relationships, then composed with the GFM for downstream prediction tasks. It is not a generic score or a one-off downstream head; it is the private foundation layer from which your task-specific models are trained.

* **Pre-trained on your relational schema** — the entities you care about, the edges between them, and how both evolve over time
* **Self-supervised** — learns from the structure itself, before you supply labels
* **Composable with the GFM** — when an entity in your data resolves to an entity in our graph, both representations are available downstream
* **Yours alone** — weights are workspace-isolated and never shared across customers

## Deployment options

The RFM is the layer most likely to touch sensitive data. We designed it accordingly.

<CardGroup cols={2}>
  <Card title="Managed by Avra" icon="cloud">
    The fastest path. Your data flows into a tenant-isolated environment, the RFM is trained and served by Avra, and downstream models are available through the API the same day.
  </Card>

  <Card title="Deployed in your environment" icon="building-shield">
    For data residency, regulatory, or sovereignty requirements: the RFM trains and runs inside your perimeter. Avra provides the training stack and the operator tooling; the data never leaves.
  </Card>
</CardGroup>

## The flywheel

The RFM gets stronger with every downstream model you train.

Each downstream task — a credit model, a churn model, a custom classifier — generates signal about which patterns in your data predict the outcomes you care about. That signal flows back into the RFM, refining its representation. The next downstream task starts from a stronger base.

This is what compounds. A frozen foundation is a one-time gift. A foundation that learns from every downstream task you train is an asset that appreciates.

## Lifecycle

<Steps>
  <Step title="Schema declaration">
    You declare the entities, edges, and identifiers in your relational data. Avra reconciles them against the entities the GFM already understands.
  </Step>

  <Step title="Ingestion">
    Stream or upload data through API, connectors, or SFTP. Each event is validated against the declared schema; deviations trigger a notification, not a silent failure.
  </Step>

  <Step title="Pre-training">
    The RFM is pre-trained on your relational schema and temporal business data, with strict temporal validation so the model never learns from the future.
  </Step>

  <Step title="Downstream training">
    Task-specific models are trained on top of the RFM and GFM together. Signal from each task is fed back into the RFM.
  </Step>

  <Step title="Promotion">
    New RFM and downstream snapshots are validated offline, mirrored against production traffic, and promoted only after meeting your performance bar.
  </Step>
</Steps>

## Data ownership

* Your raw data remains your exclusive property and is stored in dedicated tenant-isolated buckets.
* RFM weights are exclusive to your workspace and are never shared with or used to serve other customers.
* Audit exports let you inspect what data influenced any given snapshot.
* On-premise deployments give you full physical custody — no data crosses the boundary.

See [Data Privacy and Compliance](/security/data-privacy-and-compliance) for the full policy.