Getting data from PDFs into Salesforce can be a real challenge. For example, imagine your proptech company analyzes leases, but your company’s process for getting lease data into Salesforce is slow. With Sensible's beta Zapier integration, you can take a low-code approach to transforming data in PDFs and other documents into Salesforce records, as well as emails, databases, Google sheets, and other supported Zapier destinations.
Learn more about Human Review: a powerful new feature that allows you to easily add manual oversight to your document extraction process.
Extract structured data from financial documents in seconds
Automate data entry from loss runs, ACORD forms, policies, and more
Parse structured data from offering memos, rent rolls, and more
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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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.
Explore how Sensible's SenseML streamlines Prop Tech by simplifying rent data extraction from rent rolls. This guide offers step-by-step instructions for incorporating Sensible's document extraction tools into your product.
After you extract document data with our developer platform, you can use custom logic to transform the extraction to add or remove data or to conform to your desired schema.
Explore how Sensible's SenseML streamlines HR Tech by simplifying candidate data extraction from resumes. This guide offers step-by-step instructions for incorporating Sensible's document extraction tools into your product.
How Sensible extracts structured data from documents.
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We’ve recently explored some new approaches to retrieval-augmented generation (RAG) that rely solely on completions without using embeddings. Learn how this completions-only method compares to embedding-based approaches, and why we believe it may be the future for certain RAG use cases as language models continue to improve.