Earlier this year, we introduced Navi as a new interface to the Parsewise Data Engine. Instead of learning the platform first, users could begin with the questions they wanted answered and the data sitting in their documents.
Navi can now build apps around a project s extracted data, inspect the decisions behind an extraction, transcribe dictated prompts, and use screenshots as context.
Excited to launch the Parsewise MCP You can now connect Parsewise directly into tools like Claude, Cursor, and other LLM tools. That means you can ask questions across your documents, extract structured data, and pull answers with citations, without leaving your workflow. Here is a video on YouTube with an example of how it's used: Parsewise MCP Demo
One API call replaces the entire document processing pipeline.
You send multiple documents and a desired output schema; you get back resolved values, flagged contradictions, and full lineage down to the source words, pages, and documents, with bounding boxes you can embed directly into your own UI for human validation.
No parsing, classification, stitching, or custom verification interface to build or maintain.
Parsewise deploys AI agents that analyze entire document corpora – thousands of documents, one run. Instead of prompting single PDFs, agents extract, cross-reference, and reason across the entire batch, with every output anchored to its exact source for full traceability. No more black box reasoning. Users configure and launch agents without code, across any document type.
No black boxes. No engineering. No bottlenecks.
