> ## 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.

# Applied AI Overview

> Design and deploy supervised learning workflows for production AI applications across healthcare, manufacturing, smart cities, and more.

Encord helps applied AI teams build **annotation and evaluation workflows for production-ready AI applications**. Whether you're iterating on a new model or closing the loop between production failures and training data, Encord connects your data, human reviewers, and models in a single platform.

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## Who this is for

Applied AI teams that need to:

* Build reliable, repeatable annotation workflows for supervised learning
* Evaluate model performance and trace failures back to training data
* Coordinate ML engineers, data teams, and annotation workforces
* Continuously improve models as production data evolves

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## Key capabilities

### Annotation for production AI

Label any combination of image, video, audio, text, and DICOM data with tools optimized for accuracy and throughput.

* **Images** — bounding boxes, polygons, polylines, keypoints, bitmasks, and object primitives; use SAM 2 natively to segment and classify objects up to **10x faster**
* **Video** — video-native annotation with temporal context, automated object tracking, and single-shot labeling across scenes; label up to **6x faster**
* **Audio** — transcription, classification, and sequence labeling
* **Text and documents** — entity recognition, classification, and structured extraction
* **DICOM / medical imaging** — specialized tooling for clinical and research annotation

AI-assisted labeling integrations include **GPT-4o, LLaMA 3.2, Gemini**, SAM 2, YOLO, and your own custom models.

### Human-in-the-loop (HITL) workflows

Build annotation and review pipelines that combine human judgment with model automation.

* Route tasks through multi-stage Workflows: annotate → review → QA
* Assign tasks by role, skill, or dataset
* Use consensus labeling to measure inter-annotator agreement and surface disagreements
* Escalate edge cases for expert review

### Model evaluation and debugging

Understand where your models fail and what data to collect or re-label next.

* Import model predictions and compare against ground truth labels
* Automatic reporting on **mAP, mAR, F1 Score**, and other metrics
* Identify underperforming clusters, edge cases, and underrepresented classes
* Surface the exact data that led to unexpected model behavior

### Active learning and continuous improvement

Turn production deployment into a training signal.

* Route low-confidence predictions back into annotation queues
* Track failure modes and underrepresented patterns across production data
* Continuously tighten your training distribution as requirements evolve
* Version datasets and track ground truth against each model iteration

### Multimodal and cross-team collaboration

Encord supports the full range of applied AI data types and coordinates work across distributed teams.

* Unified platform for ML engineers, data managers, and annotators
* Fine-grained role-based access control
* Shared Ontologies and reusable Workflow templates across Projects
* Dataset versioning and label export in standard formats

### API and SDK integration

Encord integrates into your existing MLOps stack.

* Python SDK for programmatic access to Projects, Datasets, and labels
* Automate data pipelines and trigger workflows from external systems
* Export labels in JSON, COCO, and other formats
* Webhook notifications for task-level events

See the [SDK documentation](/sdk-documentation) for full reference.

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## Common use cases

| Industry           | Use case                                               |
| ------------------ | ------------------------------------------------------ |
| Healthcare         | Medical image annotation (DICOM, radiology, pathology) |
| Manufacturing      | Defect detection and quality control                   |
| Smart cities       | Object detection and tracking in video                 |
| Sports analytics   | Pose estimation and player tracking                    |
| Autonomous systems | Sensor fusion annotation for camera and LiDAR data     |
| Retail             | Product recognition and shelf compliance               |

***

## Where to go next

* [Data Lifecycle](/solutions-documentation/applied-ai/data-lifecycle) — how data moves through ingestion, curation, annotation, and export
* [End-to-End Walkthrough](/solutions-documentation/applied-ai/end-to-end-walkthrough) — a complete example from raw data to exported labels
* [Platform documentation](/platform-documentation/GettingStarted/gettingstarted-welcome) — feature-level documentation for Annotate, Index, and Active
* [SDK documentation](/sdk-documentation) — automate and integrate with the Encord API