Parsewise Documentation

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Parsewise extracts structured data from PDFs, Office files, and images using AI-powered agents, and returns it with page-level citations.

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What you can do

  • Extract structured fields from unstructured documents with page-level citations.
  • Organise results into spreadsheet-style tables, sliced by custom dimensions.
  • Compute reusable metrics and scores with derived agents.
  • Shape agent results into reusable JSON with an Output Schema.
  • Chat with your documents and extracted data through Navi, with inline citations.
  • Build project-specific interactive interfaces with Navi Apps.
  • Drive the whole workflow from a REST API.

Supported file types

PDFs, Microsoft Word (.docx, .doc), Excel (.xlsx), PowerPoint (.pptx), email (.eml), and images (PNG, JPG). Enterprise customers can enable additional file types — email support@parsewise.ai if you don’t see yours.

Explore the docs

Getting Started

End-to-end walkthroughs for the app and the Python API.

Platform

Concepts, agent design, plans, and security.

Projects and Documents

Create and organise projects, upload files, and review parsed documents.

Dimensions and Row Shape

Control whether agents produce project-wide, per-entity, or per-document results.

Using Navi

Ask cited questions, analyse results, and create agents conversationally.

Working with Results

Filter values, inspect sources, resolve issues, and export data.

Derived Agents

Compute persistent values from other agents’ resolved results.

Testing and Evaluating Agents

Run quality checks, compare against golden sets, and score criteria.

Apps and Navi Apps

Use built-in workflows or create interactive interfaces with Navi.

Output Schema

Shape agent results into JSON for downstream systems.

Parsewise MCP

Connect Cursor, VS Code, Claude, and other AI clients to Parsewise.

API Reference

Authentication, endpoints, end-to-end Python examples, and FAQ.

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