The breakthrough that transformed language applies to economic intelligence.
Language models demonstrated that pre-training on massive corpora produces representations that transfer to almost any downstream task with minimal supervision. A model that has read the world understands language well enough to be adapted into anything that uses it.
The Graph Foundation Model (GFM) applies the same paradigm to relationships. Pre-trained on a graph of companies, individuals, assets, and the events that connect them, the GFM develops a deep representation of how entities behave, connect, and evolve — before it sees a single label from your business.
A language model doesn’t relearn English for every task. The GFM doesn’t relearn what a healthy company looks like for every customer. It already knows. You define what matters for your business; the foundation does the rest.
Current specialization, expanding scope
Today the GFM is specialized for Brazil. It encodes Brazilian entity taxonomies, corporate structures, judicial and regulatory dynamics, and the specific shape of how relationships form in the local economy. That depth is the moat — generic graph models cannot match it on Brazilian decisions.
The same architecture extends. New regions become new GFM specializations, each pre-trained on the relational substrate of its own economy.
What pre-training delivers
Cold-start, solved
Traditional models fail on new and thin-file entities because they have no history. Pre-training changes the default.
| Scenario | Traditional model | Pre-trained GFM |
|---|
| New entity, no financials | Reject or guess | Infer from network position, similar entities, sector patterns |
| Individual with no record | Reject or guess | Infer from relationships, geography, behavioral similarity |
| Niche sector | Poor coverage | Cross-sector pattern transfer |
Time-to-value in days
Without pre-training, every new task starts from zero — months of data collection, feature engineering, and training before anything ships. With pre-training:
- Days, not months, to deploy a working model
- Smaller labeled datasets required to fine-tune downstream
- Better performance from the first iteration, because the foundation starts with real understanding
One foundation, many tasks
Credit, fraud, growth, churn, custom classification — the same pre-trained representations underlie all of them. Each task adds only a lightweight downstream head on top of a foundation that already understands entities.
Solve one problem with Avra and the next one is already half-solved.
Pair it with the RFM
The GFM understands the world. Your Relational Foundation Model understands your business. Downstream models inherit from both.
The combination — public economy plus your private graph — is what makes Avra predictions structurally different from bureaus, internal models, or generic ML platforms.
Versus other approaches
| Approach | Pre-training | Personalization | Graph-native |
|---|
| Bureaus | Historical payment data | None | No |
| In-house ML | Your data only | Full control | Rarely |
| Generic LLMs | Text corpora | Prompting | No |
| Avra | Relational economy + your business graph | Tenant-isolated, per-customer | Yes |
