Morphik addresses the limitation of text-only RAG systems by providing retrieval-augmented generation that understands visual content alongside text. Traditional RAG pipelines extract text from documents and lose the information contained in charts, diagrams, tables, images, and spatial layouts. Morphik processes these visual elements natively, enabling retrieval of information that exists in graphical form and combining visual and textual context for more complete answers.
The document processing pipeline handles complex layouts including multi-column academic papers, financial reports with embedded charts, technical documentation with diagrams, and presentation slides with visual content. Tables are extracted with structure preserved, charts are interpreted for their data content, and images are analyzed for relevant information. This multimodal processing produces richer knowledge bases than text-only alternatives.
Backed by Y Combinator with over 3,600 GitHub stars, Morphik provides an API-first platform for building multimodal knowledge bases. The ingestion API accepts PDFs, images, presentations, and other document formats. The retrieval API returns relevant content with both textual and visual context. Integration with LLM applications enables question-answering that references visual evidence alongside text passages, critical for domains like healthcare, finance, and engineering where information is often communicated visually.