Extract Structured Data From Any Document Information Extract Api Is Live

Extract structured data from any document—Information Extract API is live

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One month ago, we opened a playground for Information Extract . The waitlist filled fast, and developers tested it with real-world documents—insurance packets, scanned forms, multipage tables. This early traffic helped us refine schema alignment, layout handling, and batch performance. Today, Information Extract becomes a production-ready REST API —turning unstructured PDFs into structured, schema-aware JSON. No training. No templates. No prompt tuning. What makes Information Extract different Zero-training extraction : Works on any document—no templates, no fine-tuning required Schema-aligned output : Returns structured JSON that matches your schema—types, nesting, and required fields included Layout understanding : Accurately handles tables, checkboxes, multi-page layouts, and rotated content Flat per-page pricing : Predictable billing, regardless of token count or content complexity

From document to JSON—in one call Information Extract turns layout-heavy PDFs into clean, typed JSON—aligned to your schema, without templates or scripting.

In this example, a multi-page rent roll PDF is converted into structured JSON. Each row is mapped to typed fields like rent, deposits, concessions, and parking fees—with no templates or custom scripts. How to extract with schema in one call

Information Extraction Request using the generated schema

extraction_response = client.chat.completions.create( model= "information-extract" , messages=[ { "role" : "user" , "content" : [ { "type" : "image_url" , "image_url" : { "url" : f "data:image/png;base64,{base64_data}" } } ] } ], response_format={ "type" : "json_schema" , "json_schema" : { "name" : "document_schema" , "schema" : { "type" : "object" , "properties" : { "bank_name" : { "type" : "string" , "description" : "The name of bank in bank statement" } } } } } ) Available now Upstage Console : Create a workspace and get $10 free credits

Let your apps understand documents—at scale. Start building with the Information Extract API. ‍

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Upstage enables flexible, schema-driven data extraction that adapts to changing ontologies—unlike rigid systems that fail when schemas or regulations shift.

Tutorials October 21, 2025

How Information Extract works in 3 Steps

Upload ACORD forms, loss runs, and policy docs as-is. Define a schema and get grounded, source-cited JSON in minutes—ready for underwriting and submissions.

Announcements April 3, 2025

Demo now open! Unleash information from any document

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One month ago, we opened a playground for Information Extract . The waitlist filled fast, and developers tested it with real-world documents—insurance packets, scanned forms, multipage tables. This early traffic helped us refine schema alignment, layout handling, and batch performance. Today, Information Extract becomes a production-ready REST API —turning unstructured PDFs into structured, schema-aware JSON. No training. No templates. No prompt tuning. What makes Information Extract different Zero-training extraction : Works on any document—no templates, no fine-tuning required Schema-aligned output : Returns structured JSON that matches your schema—types, nesting, and required fields included Layout understanding : Accurately handles tables, checkboxes, multi-page layouts, and rotated content Flat per-page pricing : Predictable billing, regardless of token count or content complexity

From document to JSON—in one call Information Extract turns layout-heavy PDFs into clean, typed JSON—aligned to your schema, without templates or scripting.

In this example, a multi-page rent roll PDF is converted into structured JSON. Each row is mapped to typed fields like rent, deposits, concessions, and parking fees—with no templates or custom scripts. How to extract with schema in one call

Information Extraction Request using the generated schema

extraction_response = client.chat.completions.create( model= "information-extract" , messages=[ { "role" : "user" , "content" : [ { "type" : "image_url" , "image_url" : { "url" : f "data:image/png;base64,{base64_data}" } } ] } ], response_format={ "type" : "json_schema" , "json_schema" : { "name" : "document_schema" , "schema" : { "type" : "object" , "properties" : { "bank_name" : { "type" : "string" , "description" : "The name of bank in bank statement" } } } } } ) Available now Upstage Console : Create a workspace and get $10 free credits

Let your apps understand documents—at scale. Start building with the Information Extract API. ‍

One month ago, we opened a playground for Information Extract . The waitlist filled fast, and developers tested it with real-world documents—insurance packets, scanned forms, multipage tables. This early traffic helped us refine schema alignment, layout handling, and batch performance. Today, Information Extract becomes a production-ready REST API —turning unstructured PDFs into structured, schema-aware JSON. No training. No templates. No prompt tuning. What makes Information Extract different Zero-training extraction : Works on any document—no templates, no fine-tuning required Schema-aligned output : Returns structured JSON that matches your schema—types, nesting, and required fields included Layout understanding : Accurately handles tables, checkboxes, multi-page layouts, and rotated content Flat per-page pricing : Predictable billing, regardless of token count or content complexity

From document to JSON—in one call Information Extract turns layout-heavy PDFs into clean, typed JSON—aligned to your schema, without templates or scripting.

In this example, a multi-page rent roll PDF is converted into structured JSON. Each row is mapped to typed fields like rent, deposits, concessions, and parking fees—with no templates or custom scripts. How to extract with schema in one call

Information Extraction Request using the generated schema

extraction_response = client.chat.completions.create( model= "information-extract" , messages=[ { "role" :...