---
title: Welcome to Avra
description: 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.

:::info
**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**

- **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

1. **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.

2. **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.

3. **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 |

## Explore

**[Why Avra](/pt-BR/why-avra/the-challenge)**

The problem we solve and why our approach works

**[Platform Architecture](/pt-BR/platform)**

The three layers, the flywheel, and how they compose

**[API Reference](/api-reference)**

Endpoints, authentication, and integration patterns

**[Use Cases](/pt-BR/use-cases)**

Credit, fraud, growth, and any task you can label