Over the past four months, LandingAI’s Agentic Document Extraction tool has seen rapid adoption across industries. Motivated by this momentum, our team has been hard at work expanding the tool’s capabilities. We’re excited to introduce a major new feature: Field Extraction.
What Is Field Extraction?
Field Extraction enables you to extract structured data directly from documents—such as invoices, patient records, or insurance forms—by specifying exactly which fields you want to capture. Instead of manually searching and copying data, you can now automate this process at scale, saving time and reducing errors.
Why Field Extraction Matters
- Efficiency: Automate repetitive data extraction tasks across thousands of documents.
- Accuracy: Reduce manual errors and ensure consistent, validated data.
- Transparency: Visual grounding lets you trace every extracted field back to its source.
- Flexibility: Customizable schemas adapt to any document type or business workflow.
Real-World Use Cases
- Healthcare: Extract structured data from medical forms, clinical diagrams, and patient records.
- Finance: Automate extraction of tables, charts, and key fields from invoices and financial statements.
- Legal: Process and extract information from contracts and agreements with complex layouts and embedded exhibits.
Watch Andrew Ng’s walkthrough using ADE Python Library
How Does It Work?
- Simply upload your document (e.g., an invoice) to the Agentic Document Extraction interface.
2. The system automatically suggests a set of fields to extract—such as Invoice Number, Date, Vendor Name, and Line Items.
- Each field is defined in a schema (e.g., billing name as a string, line items as an array).
- You can accept the suggested schema or customize it to fit your specific requirements.
3. Run the extraction. In seconds, the tool pulls out the specified fields (e.g., invoice number, total amount, itemized list).
- Visual Grounding: Click any extracted value to see exactly where it was found in the original document. This builds trust and transparency into your data pipeline.
4. Integrate the schema into your code.
- When you call the API directly: include your JSON schema when you call the API—it will extract the specified fields and return structured results.
- When you use the library: the schema compiles to Pydantic classes, a standard for data validation in Python. Simply copy this code into your script.
5. Run your code. Run your code and see Agentic Document Extraction automate field extraction across batches of documents!
Get Started
Whether you’re processing invoices, medical records, or insurance forms, Field Extraction in Agentic Document Extraction will streamline your workflow and unlock new efficiencies.
We hope you find this new capability useful, and we can’t wait to see the innovative ways you’ll use it!
Ready to try Field Extraction? Get started with Agentic Document Extraction today!
Have questions or feedback? Join our Discord community or contact Us.
