Reducto leads independent benchmark on structured extraction with Deep Extract

"One of our engineers requested a specific feature from the Extract API, and there was a one day turnaround time for the Reducto team to ship the feature which is crazy to me."

Anuj Iravane
AI Research Lead at Anterior

"Ingestion is the bottleneck for making our products valuable in real use cases … Reducto is our ingestion team."

Engineering Leader
Fortune 10 Enterprise

"Document processing was one of the foundational problems we had to solve and build upon."

Dylan Hanson
Founding Engineer at Elysian

Features

• Intelligent chunking
• Figure summarization
• Graph extraction
• Automatic page rotation
• Embedding optimization

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Open the cookbook
Cookbook Build it yourself: extract every field from a handwritten property loss form
Pull every field from a handwritten first notice of loss form, from scribbled corrections to structured data.
Open the cookbook

Read the story
Case study Elysian: 16x faster insurance claims review
How Elysian uses Reducto to review insurance claims 16x faster.
Read the story

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Open the cookbook
Cookbook Parse a loss run report into clean claims tables
Turn carrier loss run reports into clean, structured claims tables no matter the layout.
Open the cookbook

Read the blog
Blog Intelligent Document Processing
Learn how intelligent document processing turns insurance paperwork into reliable workflows.
Read the blog

Applications

• Semantic search and RAG: Ask grounded questions across policies, claims files, ACORD forms, underwriting submissions, and specialty/reinsurance slips.
• Underwriting intake: Extract risk factors, protections, exposures, limits, and timelines from submissions and supporting documents to speed up decisions.
• Specialty & reinsurance processing: Parse slips for treaty terms, line sizes, exclusions, currencies, and loss histories with clause-level fidelity for tracking.
• Fraud signal extraction: Surface inconsistencies, duplicated evidence, date mismatches, and conflicting statements across claims packets to detect fraud early.
• Evidence packet structuring: Organize photos, adjuster notes, estimates, invoices, and correspondence into event-driven, chronologically aligned evidence sets.
• CAT event aggregation: Ingest photos, field reports, and adjuster notes from catastrophic events to build unified views of affected properties and losses.