Welcome to Avra
Pre-trained foundation models for relational intelligence — composed into every decision your business makes.
The Decision Intelligence Platform
Every enterprise decision — approve this loan, flag this transaction, prioritize this lead — is a question about an entity in a network. The economy is not tabular. Companies are not rows in a table; they are nodes in a graph, and that graph is the signal.
Avra is a frontier AI lab. We pre-train foundation models on relational data — the economy as a whole, and your business specifically — and compose them into the decisions your existing systems already make.
The three layers
Graph Foundation Model
Pre-trained on 1B+ entities and the relationships between them. Today specialized for Brazil, expanding to other regions. The world your business operates in.
Relational Foundation Model
A customer-specific relational representation layer, pre-trained on your schema and temporal business data, then composed with the GFM for downstream models.
Downstream Models
Task-specific models — credit, fraud, growth, custom — built on both foundations. Every model you train improves the RFM that produced it.
Across the customer lifecycle
The same foundations power intelligence at every stage:
Acquire
- Lead Scoring — identify high-value prospects before they convert
- Paid Media Optimization — enrich pixel data with entity-level signal
- Field Sales Ranking — order opportunities for maximum efficiency
Onboard
- Fraud Prevention — network-based detection before losses occur
- Risk Assessment — understand who you are doing business with
- Entity Verification — resolve and verify entities at scale
Manage
- Credit Decisions — dynamic risk assessment with trajectory analysis
- Portfolio Monitoring — early-warning signals across your book
- Relationship Intelligence — understand the networks your customers operate in
Retain
- Churn Prediction — identify at-risk relationships early
- Lifetime Value — understand long-term potential
- Custom Tasks — any prediction target you can label on entities in the graph
How it works
Pre-trained foundations
The GFM is already trained on the relational economy. The RFM is pre-trained on your relational schema and temporal business data — either by us or inside your environment.
Downstream training
Task-specific models — credit, fraud, growth, custom — are trained on top of both foundations. Signal from each training run feeds back into your RFM.
Decisions, in production
Query real-time APIs or run batch jobs. Predictions, scores, and embeddings — versioned, governed, and integrated into your existing decision systems.
Why this is different
| Traditional approach | Avra |
|---|---|
| More data = better results | Better representations = better results |
| Manual feature engineering | Foundations learn representations automatically |
| Isolated entity analysis | Network-aware intelligence |
| Static snapshots | Temporal trajectories |
| One model per use case | Two pre-trained foundations, many downstream models |
| Frozen at delivery | Flywheel — every downstream model improves the foundation |