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Avra is built on a simple thesis: the economy is not tabular. Companies are not rows in a table — they are nodes in a network, and that network is the signal. We operationalize that thesis with three composable foundation layers. Each one is pre-trained. Each one carries forward to the next.
1

Graph Foundation Model (GFM)

A foundation model pre-trained on the relational economy. It learns how entities behave, connect, and evolve before it ever sees your data. Today it is specialized for Brazil; the same architecture extends to other regions as we expand.
2

Relational Foundation Model (RFM)

A customer-specific relational representation layer, pre-trained on your schema and temporal business data, then composed with the GFM for downstream models. Available as a managed service or deployed inside your environment.
3

Downstream Models

Task-specific models — credit, fraud, growth, custom prediction targets — trained on top of the GFM and RFM. They inherit network intelligence from both. Every downstream model trained also improves the RFM that produced it.

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The shape of it

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The flywheel

The three layers are not stacked once and frozen. They compound. Each downstream model you train surfaces new signal — what predicted churn, what predicted default, what predicted conversion. That signal is fed back into the RFM, refining its representation of your business. The next downstream model starts from a stronger base. The next one after that, stronger still. The RFM learns from outcomes the GFM never saw. The GFM keeps the RFM grounded in the broader economy. Downstream models inherit both — and contribute back.
One foundation, trained once. A second foundation, trained on you. Every model after that, stronger than the last.

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Why three layers, not two

A single foundation model — yours or ours — is a compromise. A general foundation alone misses your business. Your customer hierarchies, your transaction patterns, your definitions of success are invisible to a model trained on public data. You either ship a generic score or backfill that gap with brittle feature engineering on top. A customer-specific foundation alone misses the world. A model trained only on your data has no view of counterparties, supply chains, judicial events, or sector dynamics that determine outcomes. It can describe your portfolio but not the context around it. Avra trains both. The GFM brings the economy. The RFM brings your business. Downstream models compose them into decisions.

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Deployment

Different layers live in different places, by design.

Managed cloud

The full stack hosted by Avra: GFM, RFM, downstream models, inference APIs, dashboards. The default path — fastest time to production, no infrastructure to operate.

On-premise RFM

Run your Relational Foundation Model inside your own environment. Sensitive data never crosses your perimeter. The GFM continues to provide the broader economic context as a managed service.

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Composability

Each layer is useful on its own.
  • GFM embeddings plug into your existing ML pipelines as features — no fine-tuning required.
  • RFM can power your internal models even without our downstream tasks.
  • Downstream models can be queried as APIs, run as batches, or accessed through dashboards.
The three layers are designed so you can adopt one, two, or all of them — and so the value compounds when you adopt more.

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What you operate

You retain control over the parts that affect your decisions:
  • Model versions and aliases — promote, roll back, run challenger experiments
  • Data contracts — declare what you send and how it maps to entities
  • Access and isolation — workspace-scoped API keys, role-based access, audit
  • Outcomes — your labels, your definition of success, your retraining cadence
Avra operates the foundation. You operate the decisions.