Nov 14, 2025
Document AI: The Next Evolution of Intelligent Document ProcessingEntity Extraction API
[ Entity Extraction API ]
Extract Key Entities Instantly with the Entity Extraction API
Turn messy PDFs into structured JSON entities with LlamaParse’s layout-aware parsing and confidence metadata.
Extract Entities from Messy Documents into Clean JSON
LlamaParse turns PDFs, scans, and messy forms into structured entity fields you can trust, delivered as clean, schema-ready JSON. Layout-aware parsing and validation loops catch tables, headers, and edge cases, so your Entity Extraction API ships with fewer exceptions.
Best-in-Class Accuracy
Turn Complex Documents into Structured Data Across Industries
FinTech Lending Operations
Extract entities from bank statements, pay stubs, and tax forms into structured JSON—capturing tables, multi-column layouts, and line items without brittle post-processing. Standardize names, addresses, employers, and income signals with citations and confidence scores to speed underwriting while reducing manual review queues.
Healthcare & Medical Services
Pull medications, diagnoses, lab values, and procedure codes from referrals, discharge summaries, and scanned PDFs—even when critical details live in tables, headers, or embedded images. Use page-level coordinates and metadata to route exceptions to staff quickly and keep patient intake and prior authorizations moving.
Construction & Engineering Project Delivery
Convert SOWs, RFQs, change orders, and submittals into clean Markdown/JSON so teams can reliably extract part numbers, quantities, dates, and acceptance criteria from complex document layouts. Turn spec tables, marked-up drawings, and schedules into queryable entities that reduce rework and tighten bid-to-build handoffs.
Startups
Ship entity extraction from messy user-uploaded PDFs (invoices, contracts, receipts) with natural-language parsing instructions instead of months of custom rules and edge-case patches. Control burn with tier-based agentic processing and auto correction loops, so you get production-grade accuracy without enterprise headcount.
The Solution
OCR Features Built for Accurate Entity Extraction APIs
01
Layout-Aware Entity Context
LlamaParse preserves reading order and document structure across multi-column pages, headers/footers, and dense forms. That structure keeps entities tied to the right section and label, so your Entity Extraction API returns the correct “who/what/when” instead of orphaned strings.
02
Structured JSON Output Mode
Return extraction-ready JSON that’s consistent enough to feed directly into your Entity Extraction API without fragile post-processing. Each field can include page references and element-level metadata, making it easy to trace an entity back to the exact source snippet.
03
Prompted Extraction Instructions
Use natural-language parsing instructions to specify the exact entities you care about (e.g., vendor name, invoice ID, dates, line-item totals) and the shape of the output. This reduces custom regex and template logic, so you can launch new entity types quickly and keep the API stable as documents change.
04
Agentic Validation Loops
LlamaParse runs self-correction and validation steps to catch common extraction failures like swapped fields, missing totals, or inconsistent formats. That improves precision and straight-through processing, which is exactly what you want when your API is expected to return reliable entities at scale.
Technical OCR documentation
Agentic OCR, documented for builders.
Explore our developer guides to easily connect your document pipelines to LlamaParse.
Explore the documentation
Eliminate Human Error
Our AI catches the typos that tired eyes miss.
Format Flexibility
Export to Excel, JSON, XML, or directly via API.
Enterprise-Grade Security
SOC2 Type II compliant with end-to-end encryption.
No-Code Templates
Train the tool on your specific forms in minutes, not days.
Lightning Speed
Average processing time of <3 seconds per page.
LlamaParse’s support of a wide variety of filetypes and its accuracy of parsing made it the best tool we tested in our evaluations. The LlamaIndex team was very responsive and we were off to the races within a day.
Common FAQs
How Does it Work?
01
How does the Entity Extraction API handle multi-column PDFs, tables, and dense forms without mixing fields?
01
How does the Entity Extraction API handle multi-column PDFs, tables, and dense forms without mixing fields?
Our layout-aware parsing preserves reading order and document structure across columns, headers/footers, and form sections. That keeps entities tied to the correct label and context, so you get “who/what/when” reliably instead of orphaned or swapped values.
02
Can I get consistent, extraction-ready JSON without building brittle post-processing rules?
02
Can I get consistent, extraction-ready JSON without building brittle post-processing rules?
Yes—Structured JSON Output Mode returns predictable fields that are easy to consume directly in your pipeline. You can also include page references and element-level metadata, making it straightforward to trace every extracted entity back to the exact source snippet.
03
How do I customize what entities are extracted for my specific documents (invoices, contracts, claims, etc.)?
03
How do I customize what entities are extracted for my specific documents (invoices, contracts, claims, etc.)?
Use natural-language extraction instructions to specify exactly which entities you want and the shape of the output. This reduces the need for regex and template logic, so you can add new entity types quickly as requirements change.
04
What prevents common extraction errors like missing totals, swapped fields, or inconsistent date formats?
04
What prevents common extraction errors like missing totals, swapped fields, or inconsistent date formats?
Agentic validation loops run self-correction checks designed to catch and fix common failure modes before results are returned. That improves precision and increases straight-through processing, especially at scale where small error rates become operationally expensive.
05
How can my team audit results and resolve disputes when an extracted value looks wrong?
05
How can my team audit results and resolve disputes when an extracted value looks wrong?
Each entity can include source metadata such as page references and element-level context, so reviewers can verify the exact origin in the document. This makes QA faster, supports compliance workflows, and builds confidence before you automate decisions downstream.
06
How fast can we onboard and keep the integration stable as document layouts change over time?
06
How fast can we onboard and keep the integration stable as document layouts change over time?
Because extraction behavior is driven by prompts and structured output rather than hard-coded templates, you can adapt to new layouts with minimal engineering effort. Most teams start with a small set of entities, validate results, and expand coverage without breaking the API contract.
Resource & Related Sections
