Our Approach

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A foundation model reflects the universe it was trained on

GPT understands English because it was trained on English. It grasps cultural references, legal structures, and business norms because that is what it learned from. Avra understands relationships because that is exactly what we built it for. Not text, not images — the network of entities, their connections, and how both evolve over time.

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What a foundation model is

A foundation model is pre-trained once on broad data to learn general representations that transfer to many downstream tasks. Instead of building a separate model for every problem, you train one foundation and adapt it. We do this twice.

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Why graphs

Most AI models treat data as rows in a spreadsheet — each entity independent, defined by its own attributes. Entities do not exist in isolation. Entities exist in networks. Companies have suppliers, customers, partners, competitors. Individuals have employers, co-directors, family connections. Assets flow between accounts. Events cascade through ownership chains. An entity’s risk profile changes dramatically based on its network position. A clean-looking company becomes high-risk when its main supplier has active litigation from a counterparty in multiple fraud cases. Traditional models cannot see this. Graph models can.

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Graph neural networks

Graph neural networks are architectures designed to learn from connected data. Instead of analyzing each entity in isolation, they propagate signal through relationships — from each entity to its neighbors, to its neighbors’ neighbors, and so on. Every entity is understood not just by its own data, but by its position in the network. This is how we built both the GFM and the RFM.

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Why two foundations, not one

A generic foundation alone misses your business. Your hierarchies, your patterns, your definitions of success are invisible to a model trained on public data. A customer-specific foundation alone misses the world. A model trained only on your data has no view of counterparties, sector dynamics, or events that determine outcomes. Avra trains both. The GFM brings the relational economy. The RFM brings your business. Downstream models inherit from both, and contribute back.

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Today’s scope

The GFM is currently specialized for Brazil. It encodes local entity taxonomies, corporate structures, and judicial and regulatory dynamics with a depth no generic model can match. The same architecture extends to new regions — each becoming its own pre-trained GFM specialization on the relational substrate of its own economy.

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Avra is a frontier lab

We are not building one product. We are building the foundation layer for relational intelligence — the model architecture, the training infrastructure, the temporal graph, and the deployment surface that make every downstream prediction stronger. That research program is active. New foundation models, new representation techniques, new ways to compose them into decisions — shipped to production, not papers in a drawer.