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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

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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.