Launched in 2024
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Morphik is an open-source AI knowledge infrastructure platform designed to help developers and organizations build and deploy retrieval-augmented generation (RAG) pipelines over complex, multimodal data. It supports ingestion of PDFs, images, videos, and other document types, extracting structured knowledge that can be queried via natural language. Morphik provides a self-hostable server alongside a managed cloud offering, giving teams flexibility in deployment. The platform emphasizes handling documents with rich visual and textual content — such as technical manuals, research papers, and enterprise documents — where traditional text-only RAG systems fall short. It exposes APIs for integration into existing applications and workflows, and includes tooling for chunking, embedding, and semantic search. Morphik is positioned for engineering teams building AI-native products that require accurate, context-aware document retrieval at scale.

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Target audience and deployment

  • Solo / Freelancer
  • Startup
  • SMB
  • Mid-market
  • Enterprise
  • Cloud
  • Self-hosted
  • API

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Key features

Multimodal document ingestion (PDF, images, video)Retrieval-augmented generation (RAG) pipelinesSemantic search over document corporaOpen-source self-hostable serverManaged cloud deployment optionREST API for retrieval and ingestionChunking and embedding managementNatural language document queryingKnowledge graph extractionMulti-tenant data isolation

Use cases

  • Build retrieval-augmented generation pipelines over enterprise documents
  • Query multimodal documents with natural language
  • Self-host an AI knowledge base for data-sensitive environments
  • Integrate document intelligence into existing applications via API
  • Index and search technical documentation or research corpora

Best for

  • Developers who need to build accurate RAG applications over complex, multimodal documents
  • AI engineers who need to self-host a knowledge retrieval backend with full data control
  • Enterprise teams who need to query large internal document repositories using natural language
  • Startups who need to ship document-intelligence features quickly using open-source infrastructure