> ## 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.

# Quickstart

> From first conversation to predictions in production.

## Onboarding journey

<Steps>
  <Step title="Scope the engagement">
    Contact your Avra representative at **[sales@avra.ai](mailto:sales@avra.ai)**. We start by understanding the decisions you want to improve, the data you have, and the integration shape — API, batch, or embeddings — that fits your operation.
  </Step>

  <Step title="Provision your workspace">
    Avra provisions a tenant-isolated workspace. You receive credentials for [app.avra.ai](https://app.avra.ai) and access to your **Data Contract** — the agreed schema for the relational data you will send.
  </Step>

  <Step title="Send your data">
    Stream or upload your relational data through API, connectors, or SFTP. Every event is validated against your data contract; deviations trigger a notification, not a silent failure.
  </Step>

  <Step title="Train your Relational Foundation Model">
    Avra pre-trains your RFM on your relational schema and temporal business data. For managed deployments, this runs in your tenant-isolated environment. For on-premise deployments, this runs inside your perimeter.
  </Step>

  <Step title="Train downstream models">
    Task-specific models — credit, fraud, growth, custom — are trained on top of your RFM and the Graph Foundation Model. Each training run feeds signal back into your RFM, making the next iteration sharper.
  </Step>

  <Step title="Promote to production">
    Validate offline against your holdouts. Mirror against production traffic. Promote when you meet your performance bar. Roll back with one alias reassignment if anything regresses.
  </Step>
</Steps>

## Once you are live

<CardGroup cols={2}>
  <Card title="Dashboard" href="/dashboard/overview" icon="gauge">
    Monitor usage, manage models and versions, and govern access
  </Card>

  <Card title="API Reference" href="/api-reference/overview" icon="code">
    Real-time predictions, model discovery, and version control
  </Card>

  <Card title="Batch Inference" href="/data-operations/batch-inference" icon="layer-group">
    Score entire portfolios on a schedule
  </Card>

  <Card title="Embeddings" href="/solutions/embeddings/overview" icon="cube">
    Use Avra representations as features in your own ML models
  </Card>
</CardGroup>

## Where to go from here

<AccordionGroup>
  <Accordion title="I want to understand the platform first">
    Read [Why Avra](/why-avra/the-challenge), then the [Platform Architecture](/platform/architecture) — the three layers, the flywheel, and how they compose.
  </Accordion>

  <Accordion title="I want to see specific use cases">
    [Credit Intelligence](/solutions/credit-score/overview-and-methodology), [Fraud Detection](/solutions/fraud/overview), [Growth & Sales](/solutions/growth/lead-scoring), or [build your own model](/solutions/embeddings/overview) with embeddings.
  </Accordion>

  <Accordion title="I'm ready to integrate">
    Start with the [API Reference](/api-reference/overview) for authentication, endpoints, and integration patterns.
  </Accordion>
</AccordionGroup>