By Soumoshree Mukherjee
In a world where enterprises increasingly depend on AI to parse vast amounts of data, Morphik enters with a bold ambition: to enable developers to build AI tools that truly comprehend complex, visually rich documents.
Founded by Adityavardhan Agrawal and Arnav Agrawal, Morphik is an open-source RAG (Retrieval-Augmented Generation) stack designed to go beyond basic text search, bringing meaningful understanding to diagrams, CAD drawings, tables, slide decks, and scanned PDFs—formats that traditional AI models often fail to interpret.
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While conventional RAG models work well with clean text, they struggle with real-world documents where key information is embedded in visuals. Whether its engineers hunting for specs in circuit diagrams or researchers scanning lab notebooks, users often spend 50–70% of their time just locating the data. Traditional multimodal systems fall short—they often ignore visual structure or depend on unreliable, cobbled-together pipelines.
Morphik takes a new approach by embedding full-page visuals including arrows, labels, and charts alongside structured text. This lets AI models reason like human readers, capable of interpreting technical layouts and documentation. With 90% accuracy already demonstrated on arXiv QA tasks, Morphik is also adaptable to domain-specific needs through fine-tuning.
“The future of internal knowledge is navigated, not paged,” Arnav Agarwal wrote in a LinkedIn post, describing Morphik’s ability.
Morphik isn’t just theoretical it’s a robust, developer-ready stack with UI, SDK, and REST API support. Developers can quickly deploy document-aware AI agents without piecing together multiple OCR tools, vector databases, or custom processing flows. Key features include persistent KV caching to reduce inference costs, user and folder-level access controls for enterprise needs, and a flexible model registry to support diverse use cases.
The entire stack is open-source, with Morphik Core available on GitHub. The team actively invites contributions, feedback, and collaboration to grow its developer community.
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At the core of Morphik is its research agent, an AI assistant that goes beyond basic search. It links together retrieval, graph traversal, and data extraction to analyze content across tables, diagrams, and documents. Ask it something like “What’s the third pin on the USBC21 diagram?” and it will not only locate the image but interpret labels, reference surrounding context, and find additional details across pages.
This deep reasoning is critical for sectors like healthcare, law, engineering, and manufacturing—fields where precision matters. Morphik offers not just access to information, but true comprehension.
For the founders, Morphik is more than a product: it’s a shift in how AI should interact with information. As they put it, “by embedding vision directly into the retrieval layer, Morphik could become a cornerstone for AI that doesn’t just read, but truly understands.” With a growing open-source base and a developer-first mission, Morphik is poised to redefine enterprise document intelligence.