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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.

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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

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Deployment options

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

Managed by Avra

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.

Deployed in your environment

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.

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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.

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Lifecycle

1

Schema declaration

You declare the entities, edges, and identifiers in your relational data. Avra reconciles them against the entities the GFM already understands.
2

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.
3

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.
4

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.
5

Promotion

New RFM and downstream snapshots are validated offline, mirrored against production traffic, and promoted only after meeting your performance bar.

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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 for the full policy.