Morphik is an open-source multimodal search platform for AI applications, designed to serve as an AI knowledge base that offers one API for storage, search, and orchestration. Morphik specializes in technical and domain-specific search, enabling organizations to connect with any data source and ingest information in its native format—including complex diagrams, schematics, and datasheets.
How does Morphik work?
Morphik provides a unified API that allows users to store, search, and orchestrate knowledge across diverse formats. Its core capabilities include:
- Visual-first Retrieval: Embeds and searches entire documents (images and text) without relying on OCR, enabling high-accuracy retrieval for visually complex materials.
- Knowledge Graphs: Automatically builds a knowledge graph from ingested data, making relationships and context easily accessible.
- Integrations: Connects with a wide range of data sources and offers UI, SDK, and REST API access for flexible deployment.
- Open Source & On-Premise Options: Allows organizations to deploy Morphik within their own infrastructure for greater control and security.
Morphik achieves notable accuracy on industry benchmarks such as arXiv QA, making it suitable for advanced technical and scientific content discovery.
What makes Morphik unique?
Morphik stands out for its ability to handle complex, multimodal content—including diagrams, schematics, and datasheets—natively and without conversion. Its visual-first retrieval approach and automatic knowledge graph construction are designed specifically for technical and domain-specific applications, addressing limitations of traditional enterprise search and retrieval-augmented generation (RAG) systems.
Who uses Morphik?
Morphik primarily serves companies and teams building AI applications that require advanced search and knowledge orchestration across diverse content types. Notable customers include Ribera.ai and Flux Inc., who leverage Morphik's capabilities for specialized technical domains.
Leadership Team
Morphik was founded by Adityavardhan Agrawal and Arnav Agrawal. Adityavardhan previously worked at MongoDB on scalable database algorithms and graduated from Cornell University in 2022. Arnav Agrawal received the Tata Scholarship to study Computer Science at Cornell and left to build Morphik after developing a configuration compiler and teaching ML Operations and AI at Amazon Robotics.
Recent Developments
Morphik regularly shares technical insights and product updates on their blog. Recent topics include challenges in multimodal model interpretation of diagrams, open-source alternatives to AI memory features, and advances in scientific document understanding using LLMs and Morphik.
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