Nov 14, 2025
Document AI: The Next Evolution of Intelligent Document ProcessingSmart Document Extraction Schema
[ Smart Document Extraction Schema ]
Extract Accurate Data Faster with Document AI AWS Marketplace
Use LlamaParse to turn messy PDFs into verified JSON and tables, ready for your workflows.
Parse Complex Documents into AI-ready JSON and Markdown
LlamaParse turns messy PDFs, scans, and multi-column reports into clean, structured JSON and Markdown you can ship straight into Document AI on AWS Marketplace. Agentic document parsing understands layout, tables, and figures, then validates outputs with metadata so downstream automations run accurately at scale.
Best-in-Class Accuracy
Intelligent Document Processing for Every Industry
Financial Services and Insurance Operations
Turn claims packets, loan files, and policy endorsements into clean JSON with page-level citations, so underwriting and audit teams can trace every field back to the source document. LlamaParse preserves table structure in loss runs and schedules of values, eliminating the brittle post-processing that causes exceptions and slows straight-through processing.
Manufacturing and Supply Chain Procurement
Extract line items, pricing tiers, and delivery terms from vendor PDFs and scanned purchase documents without scrambling multi-column tables or part-number grids. Use natural-language parsing instructions to normalize fields across suppliers (SKUs, Incoterms, lead times) so ERP ingestion and spend analytics don’t require custom parsers per template.
Energy and Utilities Field Services
Parse inspection reports, as-builts, and compliance forms that mix diagrams, photos, and handwritten notes, converting visual content into structured outputs your maintenance systems can actually use. Auto correction loops reduce rework from messy scans, enabling faster closeout packages and more reliable regulatory reporting.
Startups Building Document-Heavy Products
Ship document ingestion on AWS fast by using LlamaParse as the agentic parsing layer, converting messy PDFs into Markdown and JSON that your app can search, summarize, and automate against. Tier-based processing and cost optimizer mode let you keep unit economics predictable while you scale from prototypes to production workloads.
The Solution
Extract Tables, Charts & Structured JSON from PDFs
01
Layout-Aware Table Extraction
LlamaParse understands real page structure—tables, multi-column text, headers/footers—and reconstructs it without scrambling reading order. For Document AI on AWS Marketplace, this means you can ingest vendor PDFs and customer uploads into consistent, model-ready outputs instead of writing brittle cleanup code for every new layout.
02
Structured JSON + Metadata
LlamaParse can emit structured JSON with rich metadata like page numbers, element types, and spatial coordinates for each extracted field. That makes it straightforward to build auditable Document AI pipelines on AWS (search, routing, and human review) where every answer can be traced back to a specific spot in the source file.
03
Multimodal Chart Understanding
LlamaParse converts charts, images, and scanned visuals into usable text representations (for example, charts to Markdown tables) instead of dropping them on the floor. On AWS Marketplace use cases like financial reports, invoices, and compliance packs, this preserves the “non-text” facts your downstream models and workflows actually need.
04
Auto Mode Cost Routing
LlamaParse automatically routes simple pages through faster, cheaper parsing while upgrading only the complex pages to more capable agentic processing. In AWS Marketplace deployments, this keeps per-document costs predictable when volume spikes, without sacrificing accuracy on messy scans and table-heavy PDFs.
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 layout-aware extraction help with messy vendor PDFs and multi-column documents?
01
How does layout-aware extraction help with messy vendor PDFs and multi-column documents?
LlamaParse preserves true reading order by understanding tables, columns, headers, and footers instead of flattening everything into scrambled text. That means your Document AI pipeline on AWS Marketplace gets consistent, model-ready outputs across new templates without constant, brittle post-processing.
02
Can I get structured JSON with traceability for audit and human review workflows?
02
Can I get structured JSON with traceability for audit and human review workflows?
Yes—outputs can include structured JSON plus metadata like page numbers, element types, and spatial coordinates. This makes it easy to build auditable pipelines where every extracted field can be traced back to the exact location in the source document for review and compliance.
03
What happens to charts, images, and scanned visuals—are they ignored?
03
What happens to charts, images, and scanned visuals—are they ignored?
LlamaParse converts charts and visual content into usable text representations (such as Markdown tables) so those “non-text” facts aren’t lost. This is especially valuable for financial reports, invoices, and compliance packs where key data often lives in visuals.
04
How do you keep parsing costs predictable when document volume spikes?
04
How do you keep parsing costs predictable when document volume spikes?
Auto Mode cost routing sends simple pages through faster, lower-cost parsing and automatically upgrades only complex pages to more capable processing. You get stable per-document economics during spikes without sacrificing accuracy on dense tables or messy scans.
05
Will this reduce the amount of custom cleanup code we maintain for every new document layout?
05
Will this reduce the amount of custom cleanup code we maintain for every new document layout?
In most cases, yes—layout-aware extraction produces consistent structure even when templates vary across vendors or customers. Teams typically spend less time patching edge cases and more time shipping reliable downstream search, routing, and extraction workflows.
06
How does this fit into an AWS-based Document AI stack for search, routing, and downstream models?
06
How does this fit into an AWS-based Document AI stack for search, routing, and downstream models?
You can ingest PDFs and scans into structured, metadata-rich outputs that are straightforward to index, route, and review within AWS workflows. The result is cleaner inputs for downstream models and higher-confidence automation because the extracted answers remain traceable to the original document.
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