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

# Solutions

> Explore how Encord supports Physical AI, Gen AI, and production-scale AI systems with end-to-end data workflows.

# Solutions

Encord helps teams build **reliable, production-grade AI systems** by treating data as a first-class asset.

This section is organized around **solutions**, not individual tools—so you can quickly understand how Encord supports your specific use case, from early experimentation to enterprise-scale deployment.

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## How to use this section

If you’re new to Encord or onboarding a large team, start here.

Each solution page:

* explains the **problem space**
* outlines **recommended workflows**
* connects the dots across ingestion, curation, annotation, and evaluation
* links you directly to the relevant Platform and SDK documentation

You don’t need to learn every feature upfront—just follow the path that matches what you’re building.

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## Our solution areas

## Physical AI

Build AI systems that **perceive and act in the real world**, where data is multimodal, temporal, and spatial.

Typical challenges:

* Multi-camera and multi-sensor data
* 3D scenes and timelines
* Complex annotation and QA workflows
* Safety-critical edge cases

Common applications include robotics, autonomy, industrial automation, healthcare imaging, and agriculture.

→ Start here if your models interact with the physical world.

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## Gen AI

Build **grounded, aligned, and evaluatable** Gen AI systems that perform reliably in real products.

Typical challenges:

* Unstructured and noisy data sources
* Hallucinations and retrieval failures
* Human feedback and preference learning
* Continuous evaluation across prompts and models

Common applications include RAG systems, assistants, multimodal LLMs, and agentic workflows.

→ Start here if you’re working with LLMs, documents, text, or multimodal Gen AI.

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## Applied AI

Operationalize AI systems in **production environments**, where iteration speed, reliability, and collaboration matter.

Typical challenges:

* Mixed data types and evolving requirements
* Coordination across ML, data, and operations teams
* Measuring quality over time
* Closing the loop between models and data

→ Start here if you’re scaling AI beyond a single team or use case.

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## Enterprise

Run AI programs **securely and at scale**, with governance, visibility, and control.

Typical challenges:

* Large distributed teams
* Security, compliance, and access control
* Workforce management and QA
* Repeatable, auditable workflows

→ Start here if you’re deploying Encord across teams, departments, or regions.

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## How the platform fits together

All solution paths are built on the same core capabilities:

* **Index** for data ingestion, organization, and curation
* **Annotate** for annotation, review, and human feedback
* **Active** for evaluation, analytics, and continuous improvement
* **Agents** for automation and model-assisted workflows

Each solution page shows how these pieces fit together for that specific problem space.

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## Where to go next

* Explore **Physical AI** to work with multimodal and 3D data
* Explore **Gen AI** for RAG, RLHF, and agentic systems
* Explore **Applied AI** for production workflows
* Explore **Enterprise** for governance and scaling

If you already know *what* you want to do, you can also jump directly to:

* **Platform** documentation for feature-level details
* **SDK** documentation for automation and integration

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## A note on iteration

AI systems are never “done.”

The goal of these solution guides is to help you build **tight, intentional feedback loops** between data, humans, and models—so your systems improve continuously as requirements evolve.