Document Extraction Developer Resources

Use Sensible’s Node SDK to directly integrate Sensible’s document extraction and classification APIs into your product.

Sensible is updating its document processing system by moving from OpenAI's text-davinci-003 model to the more efficient gpt-3.5-turbo-0613, following comprehensive testing and prompt optimization.

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.

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 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.

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 how to write JsonLogic rules to transform your extracted document data.

Converting PDF files to Excel makes the content of documents and forms more accessible to spreadsheets’ data wrangling (filtering/querying) capabilities.

Enhance your Python skills by learning six different file uploading techniques. This guide includes everything from using the requests library to setting up a Django web app for uploads.

This tutorial guides Python developers through the process of extracting structured text from PDF documents using Sensible, a developer-first platform. The guide explains the step-by-step process of creating a configuration with SenseML, Sensible's document query language, retrieving the API key, and writing Python script to call Sensible's extractions API, thus showcasing Sensible's efficiency over alternatives like manual data entry or traditional OCR tools.

This tutorial guides you through the process of creating a Python application using the GPT-3 model and testing it with several text-heavy tasks, such as summarizing text, fleshing out outlines, and writing content.

Use these techniques to minimize your extraction times.

Extract useful data from personal PDFs, like old bank statements or gas bills, with Sensible

Loss runs are full of valuable information about an entity's risk profile, but the density of information means that there's more room for human error if you're relying on manual data entry. Learn how Sensible handles a loss run's complex structure to extract clean, tabular data automatically.

Mortgage underwriting portfolios, with multiple documents per PDF, present unique challenges for data extraction. We show how to overcome those challenges.

Use SenseML to extract structured data from the ACORD 25, a certificate of liability insurance.

Use SenseML to extract structured data from a mortgage loan closing disclosure PDF.

Use SenseML to extract structured data from a trucking rate confirmation PDF.

Extract structured data from natural-language, free-text documents like leases and legal contracts with Sensible using GPT-4.

Use SenseML to extract structured data from a Medicaid explanation of benefits PDF.

Getting data from PDFs into existing databases can be a real challenge. For example, imagine your proptech company analyzes mortgage applications, but your company’s process for getting data from 1040 forms into databases is really slow. With Sensible's beta Zapier integration, you can take a low-code approach to transforming data in PDFs and other documents into emails, databases, Google sheets, and other supported Zapier destinations.

Use SenseML to extract structured data from bank statements.