> ## 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.

# Overview

> Two pre-trained foundations, composed into any relational prediction task your business can label.

## One platform, any relational prediction task

Avra is not a single-purpose product. The Graph Foundation Model and your Relational Foundation Model produce representations that transfer to any downstream task where entity context and network structure improve predictions.

Credit risk, fraud detection, and growth optimization are the most common starting points. The same platform powers churn prediction, supplier risk monitoring, portfolio segmentation, entity resolution, and any custom classification or regression target you can label on entities in the graph.

<Info>
  If you can define a target outcome on entities in our graph, we can train a downstream model for it — and that training run will improve your RFM.
</Info>

## How it works

Every use case follows the same pattern:

<Steps>
  <Step title="Define the target">
    What does "good" or "bad" mean for your business — converted vs. not, defaulted vs. not, churned vs. retained, fraud vs. legitimate.
  </Step>

  <Step title="Train the downstream model">
    Avra trains a task-specific model on top of the GFM and your RFM. Signal from this training run is fed back into your RFM, refining its representation of your business.
  </Step>

  <Step title="Deploy and govern">
    The downstream model serves predictions through real-time APIs or batch jobs. You control versions, aliases, and rollback.
  </Step>
</Steps>

The pre-trained foundations do the heavy lifting. Your labels shape the model. Your RFM compounds.

## Common starting points

<CardGroup cols={2}>
  <Card title="Credit Intelligence" href="/solutions/credit-score/overview-and-methodology" icon="chart-line">
    Multi-horizon probability of default, credit scoring, and portfolio monitoring powered by network-aware risk signals.
  </Card>

  <Card title="Fraud Detection" href="/solutions/fraud/overview" icon="shield-halved">
    Network-based fraud intelligence that catches shell entities, collusion rings, and bust-out schemes invisible to rule engines.
  </Card>

  <Card title="Growth & Sales" href="/solutions/growth/lead-scoring" icon="rocket">
    Lead scoring, paid media optimization, and field sales ranking driven by entity trajectories and network position.
  </Card>

  <Card title="Build Your Own Models" href="/solutions/embeddings/overview" icon="cubes">
    Use Avra's adaptive embeddings as features in your own ML models for full control over architecture and objectives.
  </Card>
</CardGroup>

## Beyond these use cases

The use cases above are where most customers start — they do not define the boundaries of the platform. Avra's foundations apply to any task where entity context and network structure improve predictions:

* **Churn Prediction** — identify at-risk customers through network deterioration signals
* **Supplier Risk** — monitor supply chain health through multi-hop relationship analysis
* **Entity Resolution** — disambiguate and link entities across fragmented data sources
* **Portfolio Segmentation** — cluster entities by behavioral similarity, not just firmographics
* **Custom Classification** — any binary or multi-class outcome you can label on entities in the graph

Contact your Avra representative to scope a custom downstream model for your task.