Cohere Parse 5 - Turn complex docs, tables & images into AI-ready data
by•
Parse is Cohere's document vision parsing model. It transforms unstructured data in enterprise images and documents into structured data that downstream AI agents and applications can use.
Handles OCR, tables/diagrams/images, and visual grounding via bounding boxes, across 9 languages. Deploy via API, cloud, or fully on-prem/air-gapped.
Replies
Parse is Cohere's document vision parsing model for turning enterprise files into AI-ready data.
Problem: Enterprise documents (scans, PDFs, contracts, invoices) are messy, unstructured, and full of tables, diagrams, and charts. Most AI agents and search tools can't reliably read them, so companies end up doing manual review and data entry just to make documents usable.
Solution: A document parsing model that combines OCR with multimodal understanding, turning complex files into clean, structured data that downstream AI agents and applications can actually use.
What makes it different: It doesn't just extract text, it understands layout, tables, and diagrams together, and returns visual grounding (bounding boxes) so every extracted piece of data can be traced back to its exact location on the page. That's what makes citations and source attribution possible downstream.
Key features:
OCR for scanned and digital documents
Multimodal parsing for tables, diagrams, and images embedded in documents
Visual grounding with bounding boxes for highlighting and source attribution
Support for 9 major commercial languages
Deploy via API, Model Vault, AWS SageMaker, Azure, or fully on-prem/air-gapped
Benefits: Less manual document review, more reliable AI search and retrieval, and document context AI agents can actually reason over instead of guessing at.
Who it is for: Enterprises with high document volumes (claims, contracts, invoices, reports) building internal AI search, RAG pipelines, or multimodal agents.
Use cases: Automating claims/contract/invoice processing, improving chunking and retrieval quality for semantic search, giving AI agents full document context including tables and visual grounding.
Try here
Netlify
Nice work!
Build Check
The bounding boxes are what i'd lead with here honestly. extraction quality is nearly table stakes now, but almost nothingg hands you a coordinate, so when an agent claims something from a contract you still cant show anyone the exact clause it read. thats the bit that makes it usable when it actually matters. You have seriously done greatttt mann!!