Two healthcare organizations automated their document processing using Sensible. Company A, processing cardiac device reports for organ donation screening, built 50+ extraction configurations entirely on their own through self-service, reaching production faster than almost any other customer. Company B, a pathology lab handling handwritten requisition forms, partnered closely with Sensible's team to build configurations with sophisticated normalization logic that could handle handwriting and variable scan quality. Both chose deterministic, layout-based extraction methods (Company A for transparency and independence, Company B for consistency and accuracy), while Company B also built a generalized LLM extraction configuration for edge cases.
Two companies tackled utility and telecom bill processing with Sensible, but their implementations couldn't look more different. One processes thousands of bills monthly across 2,000+ carriers, requiring complex conditional logic to handle schema variations—like associating charges with meters even when they appear in separate sections of the document. The other processes documents stretching to tens of thousands of pages, using custom engineering to intelligently split and reassemble massive files while preserving data integrity. Both chose deterministic, layout-based extraction over LLMs because their documents were too complex and their schema requirements too strict for probabilistic approaches.
Parse bank statements, pay stubs, tax forms, and more
Parse loss runs, ACORD forms, policies, and more
Parse offering memos, closing disclosures, rent rolls, and more
Parse rate confirmations, bills of lading, invoices, and more
Parse EOBs, policies, payer confirmations, and more
Leverage Sensible's solution engineers to tackle any document automation use case
Real-time bank statement processing
Extract identification details from driver's licenses
Instantly parse policy declaration pages
Extract data from utility bills in seconds
Bringing structure to unstructured data
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Sensible is SOC2 certified and HIPAA compliant
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Learn more about Human Review: a powerful new feature that allows you to easily add manual oversight to your document extraction process.
Sensible’s new Multimodal Engine uses LLMs to extract data from non-text and partial text images embedded in a document, including pictures, charts, graphs, and handwriting.
Sensible’s approach and how it works
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How Sensible extracts structured data from documents.
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With email-driven document extraction, organizations can automatically extract structured data from document attachments by simply forwarding emails to Sensible. Using LLM-based classification, the platform intelligently processes any number of attachments per email, optionally parses email bodies, and delivers comprehensive extraction results with metadata via webhook—eliminating manual document handling across industries.
We're excited to announce a significant enhancement to Sensible's document extraction capabilities with the introduction of agentic LLM workflows.
