> ## Documentation Index
> Fetch the complete documentation index at: https://docs.avra.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

<CardGroup cols={3}>
  <Card title="Graph Foundation Model" icon="globe">
    Pre-trained on 1B+ entities and the relationships between them. Today specialized for Brazil, expanding to other regions. The world your business operates in.
  </Card>

  <Card title="Relational Foundation Model" icon="circle-nodes">
    A customer-specific relational representation layer, pre-trained on your schema and temporal business data, then composed with the GFM for downstream models.
  </Card>

  <Card title="Downstream Models" icon="diagram-project">
    Task-specific models — credit, fraud, growth, custom — built on both foundations. Every model you train improves the RFM that produced it.
  </Card>
</CardGroup>

<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.
</Info>

## Across the customer lifecycle

The same foundations power intelligence at every stage:

<AccordionGroup>
  <Accordion title="Acquire" icon="user-plus">
    * **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
  </Accordion>

  <Accordion title="Onboard" icon="clipboard-check">
    * **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
  </Accordion>

  <Accordion title="Manage" icon="chart-line">
    * **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
  </Accordion>

  <Accordion title="Retain" icon="arrows-spin">
    * **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
  </Accordion>
</AccordionGroup>

## How it works

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Decisions, in production">
    Query real-time APIs or run batch jobs. Predictions, scores, and embeddings — versioned, governed, and integrated into your existing decision systems.
  </Step>
</Steps>

## 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

<CardGroup cols={2}>
  <Card title="Why Avra" href="/why-avra/the-challenge" icon="lightbulb">
    The problem we solve and why our approach works
  </Card>

  <Card title="Platform Architecture" href="/platform/architecture" icon="diagram-project">
    The three layers, the flywheel, and how they compose
  </Card>

  <Card title="API Reference" href="/api-reference/overview" icon="code">
    Endpoints, authentication, and integration patterns
  </Card>

  <Card title="Use Cases" href="/solutions/overview" icon="rocket">
    Credit, fraud, growth, and any task you can label
  </Card>
</CardGroup>