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

# Projects

A **Project** in Avigrah is a **scoped deployment of an AI system**, created from a template.

It represents a real, working instance of AI configured for a **specific dataset, context, or business boundary**.

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## Project = Scoped AI Deployment

While AI Templates define how a system works, Projects define **where and on what data it operates**.

This means multiple projects can use the same template, but differ in:

* Data sources
* Pipelines
* Integrations
* Teams
* Business context (e.g., city, client, region, or segment)

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## Example

You may use the **Customer Intelligence** template across multiple projects:

* Customer Intelligence — Ahmedabad
* Customer Intelligence — Mumbai
* Customer Intelligence — Enterprise Clients

Each project:

* Uses the same AI system design
* But operates on different data and context
* Produces independent outputs

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## What Defines a Project

A project is defined by its **scope**, not just its use case.

Typical scoping strategies include:

* Geography (city, region, country)
* Client or account
* Business unit
* Data source separation
* Testing vs production environments

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## Why Separate Projects?

Projects ensure **clean boundaries and reliable execution**.

### Data Isolation

Each project only accesses its own pipelines and data sources.

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### Independent Execution

AI runs separately for each project without interference.

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### Clear Ownership

Teams and access are assigned per project.

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### Safer Experimentation

You can test changes in one project without affecting others.

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## What “Deployed System” Means

Each project should be treated as a **fully configured, live AI system**.

This means:

* Pipelines are properly set up
* Integrations are connected
* Data is flowing correctly
* AI agents are producing outputs

A project is not just a setup — it is an **active system delivering value**.

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## Configuration Layers Inside a Project

Each project activates an AI system by combining:

### AI Template

Defines system logic and multi-agent structure

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### Data Pipelines

Prepare and structure project-specific data

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### Integrations

Connect external systems relevant to this project

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### Teams

Control who can access and use the AI outputs

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## Best Practices

### Use Projects to Define Boundaries

Create separate projects when data, users, or context should not mix.

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### Reuse Templates Across Projects

Avoid duplicating logic — reuse templates and vary configuration.

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### Separate Production and Testing

Use different projects for experimentation and live systems.

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### Keep Projects Clean and Focused

Each project should represent one clearly defined operational scope.