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

# Introduction

> Build, run, and manage AI-native data pipelines

Prophecy helps analysts generate, refine, and operationalize data flows with AI.

Describe the data flow you want, generate pipelines with AI, refine them visually or in SQL, and run recurring data flows directly on your data platform.

Generated data flows remain editable, reusable, and production-ready from the start.

Getting started is easy. Just sign up, create a project, and build your first data flow.

## Getting started

<CardGroup cols={3}>
  <Card title="Sign up for the Free Edition" icon="arrow-pointer" iconType="duotone" href="https://app.prophecy.ai/metadata/auth/signup">
    Create a free account and start building your first data flow
  </Card>

  <Card title="Quickstart" icon="rocket-launch" iconType="duotone" href="/data-analysis/getting-started/quick-start">
    Build your first AI-generated pipeline in minutes
  </Card>

  <Card title="Build with Agent" icon="sparkles" iconType="duotone" href="/data-analysis/ai/agent/agent">
    Describe data flows in natural language and generate pipelines with AI
  </Card>
</CardGroup>

## Review and refine

<CardGroup cols={3}>
  <Card title="Review generated data flows" icon="magnifying-glass" iconType="duotone" href="/data-analysis/ai/inspect-results">
    Review, validate, and refine AI-generated pipelines visually or in SQL
  </Card>

  <Card title="Concepts" icon="lightbulb" iconType="duotone" href="/data-analysis/getting-started/concepts">
    Learn core concepts like pipelines, data flows, and parameters
  </Card>

  <Card title="Use parameters and reusable data flows" icon="sliders" iconType="duotone" href="/data-analysis/development/parameters/parameters">
    Build flexible data flows that adapt across teams, schedules, and runtime environments
  </Card>
</CardGroup>

## Extend existing data flows

<CardGroup cols={3}>
  <Card title="Share and reuse data flows" icon="share-nodes" iconType="duotone" href="/data-analysis/collaboration/project-sharing">
    Share projects and reusable data flows across teams
  </Card>

  <Card title="Schedule and run data flows" icon="clock" iconType="duotone" href="/data-analysis/production/scheduling/scheduling">
    Run recurring data flows on schedules or trigger them automatically
  </Card>

  <Card title="Import data flows" icon="file-import" iconType="duotone" href="/import-tool">
    Import existing Alteryx data flows and extend them with AI
  </Card>
</CardGroup>

## Why teams use Prophecy

Prophecy helps data teams move from ad-hoc analysis to reliable operational data flows faster with AI.

* **Build with natural language**: [Describe the data flow you want](/data-analysis/ai/agent/chat/using-chat), and Prophecy generates [pipelines](/data-analysis/development/pipelines/data-analysis-pipelines) you can refine visually or in SQL.
* **Review and refine data flows**: [Validate](/data-analysis/ai/inspect-results) generated logic and customize data flows visually or in SQL.
* **Run reliable operational data flows**: Schedule data flows, [monitor execution](/data-analysis/production/monitoring), and manage recurring data processes.
* **Share data flows across teams**: [Collaborate](/data-analysis/collaboration/project-sharing) on recurring reporting and data preparation data flows across [projects](/data-analysis/development/projects/create-project) and [teams](/administration/management/users/team-user-provisioning).
* **Review and visualize data**: [Explore outputs](/data-analysis/development/runs/data-explorer/data-explorer) and [generate dashboards](/data-analysis/analysis/overview) directly from your data flows.

## Built for analysts and data teams

### Analysts

* Build SQL pipelines and recurring data flows
* Generate and modify transformations using natural language
* Turn ad-hoc analysis into reusable operational data flows

Start here:

* [Quickstart](/data-analysis/getting-started/quick-start)
* [Build with Agent](/data-analysis/ai/agent/agent)
* [Build SQL pipelines](/data-analysis/development/pipelines/data-analysis-pipelines)

### Engineers

* Build Spark-native pipelines using visual or code-first data flows
* Develop production data flows for ingestion, transformation, and orchestration
* Deploy and monitor data flows running on your cloud data platform

Learn more:
[Data Engineering docs](/data-engineering/getting-started)