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Document Processing API | Parsewise

Document Processing API | Parsewise

AI

RESTful document-processing API for structured data extraction and cross-document reasoning to integrate document intelligence into apps.

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About Document Processing API | Parsewise

Parsewise Document Processing API provides a RESTful interface to ingest documents, extract structured data, and perform cross-document reasoning. It transforms unstructured documents into machine-readable structured outputs (fields, entities, and relations) and links information across multiple documents to support queries and downstream workflows. The API is designed for easy integration into existing stacks so teams can add document intelligence—extraction, search, and cross-document linking—without building models or pipelines from scratch.

Screenshots

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Key Features

RESTful API: A simple HTTP API to upload or reference documents and receive structured JSON outputs suitable for integration into web services, backends, and pipelines.
Structured Data Extraction: Extracts entities, key fields, tables, and relations from documents into normalized, machine-readable structures for downstream processing and analytics.
Cross-Document Reasoning: Links facts and entities across multiple documents to answer queries that require aggregation, deduplication, or inference across a document set.
Multi-format Ingestion: Accepts a variety of document formats and returns consistent structured outputs so applications can process PDFs, text, and other documents through a single endpoint.
Searchable Outputs: Produces structured records and metadata that can be indexed or used directly for semantic search, filtering, and fast retrieval in applications.
Integration-Focused Documentation: Designed for developer integration with clear REST semantics and predictable JSON responses so teams can onboard quickly and automate workflows.
Scalable Processing: Built to handle batch and high-throughput document workloads so organizations can process large document volumes without managing ML infrastructure.
Data Governance & Controls: Provides programmatic controls for document routing and structured output handling to support secure integrations and enterprise workflows.
RESTful API endpoints for document ingestion and processing
Structured data extraction (fields, entities, tables) from documents
Cross-document reasoning and aggregation across multiple documents
JSON-friendly outputs suitable for integration into apps and pipelines
Designed for integration into existing technology stacks

Use Cases

Automated Invoice Processing: Ingest invoices and extract supplier, totals, line-items, and dates into structured records for accounts payable automation.
Contract Intelligence: Extract clauses, obligations, and parties from legal documents and link related contracts to answer cross-contract queries.
Knowledge Base Construction: Convert internal reports, manuals, and documents into indexed structured records that support semantic search and enterprise Q&A.
Compliance & Audit Trails: Pull structured facts from documents and correlate them across sources to create auditable evidence for regulatory checks.
Claims Processing: Extract claimant information, policy details, and incident descriptions from submitted documents and reconcile across multiple files.
Mergers & Acquisitions Diligence: Aggregate and reason over financial statements, contracts, and reports from multiple entities to surface linked insights.
Automated extraction of structured data from invoices, receipts, and forms
Contract analysis and extraction of key clauses/terms across a corpus
Building searchable knowledge bases from collections of documents
Cross-document reconciliation and entity linking for compliance and auditing
Feeding extracted structured data into downstream workflows and analytics

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