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.
The flywheel: every downstream model you train generates signal that flows back into your RFM. The next model starts from a stronger base. The longer you run on Avra, the larger the gap between what you can predict and what anyone else can.
Across the customer lifecycle
The same foundations power intelligence at every stage:Acquire
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
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
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
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.
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 |
Explore
Why Avra
The problem we solve and why our approach works
Platform Architecture
The three layers, the flywheel, and how they compose
API Reference
Endpoints, authentication, and integration patterns
Use Cases
Credit, fraud, growth, and any task you can label