Unstructured data trapped in PDFs and image files is a massive blind spot for enterprise data systems. Historically, developers had to rely on legacy OCR—a rigid, coordinate-based approach that guaranteed pipeline failures the second a column shifted or a font changed.
Today’s document extraction software fundamentally changes that architecture. By leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), extraction is now treated as a semantic reasoning problem rather than a spatial one. These intelligent parsers understand document hierarchy and context. For developers building RAG pipelines, automating document workflows, or tackling gnarly nested tables, this means moving away from brittle, rule-based scripts and instantly getting high-fidelity, machine-readable data structures you can actually use
Leading Document Extraction Platforms
| Company | What it’s Best At | Common Use Cases | API / Integration Notes |
|---|---|---|---|
| LlamaParse (LlamaIndex) | Layout + context-aware parsing, schema extraction, multi-modal support, confidence scoring, traceability | Finance, invoices, insurance claims, recruiting | Strong Python & TypeScript SDKs; supports iterative schema development |
| Reducto | Layout-aware extraction, figure/graph summarization, agentic error correction | Investor decks, medical records, legal formatting | Typically paired with other tools for indexing |
| Google Document AI | Pre-trained processors, human-in-the-loop validation, knowledge graph integration | Procurement, government forms, large-scale classification | Deep Google Cloud Platform (GCP) integration |
| Amazon Textract | Forms/tables extraction, query-based extraction, handwriting recognition | Lending/mortgage, healthcare intake, tax documents | Supports natural-language queries |
| ABBYY | OCR skill library, NLP extraction, low-code orchestration | Supply chain, banking compliance, underwriting | Marketplace of pre-built models |
| Unstructured | Partitioning + metadata extraction, broad file format support | RAG ingestion, data lake transformations, migrations | Available as open-source and managed offerings |
| UiPath | Data extraction + full workflow automation (RPA), validation station | AP automation, HR onboarding, support operations | Best suited if already using UiPath ecosystem |
| Hyperscience | High-accuracy forms processing, messy scans/handwriting, quality control | Government workflows, archives, claims triage | Deployment options: on-prem, private cloud, SaaS |
| Landing AI | Vision-first extraction, high-resolution processing, data-centric labeling | Engineering drawings, labels, geospatial documents | More focused on computer vision than semantic NLP |
| Extend | Agentic workflows + LLM-native parsing, rapid API deployment | Credit underwriting, onboarding, vendor risk | Higher cost per document due to LLM-heavy processing |
1. LlamaParse (LLamaIndex)
Platform summary
LlamaParse is redefining enterprise document extraction by moving beyond brittle OCR. It provides an end-to-end platform for document understanding using an agentic approach to interpret complex documents and output structured data.
Key benefits
- Semantic, agentic extraction for nested tables, charts, and multi-modal content
- Developer-first Python + TypeScript SDKs, flexible schema development
- Enterprise-grade security (including VPC options) + field-level traceability
- Scales to millions of pages
Core features
- Layout + context-aware parsing (headers, multi-page tables, charts, handwriting)
- Schema-based extraction (define or auto-detect JSON schemas)
- Confidence scoring + source traceability (audit-ready)
- Multi-modal + multi-field extraction
Primary use cases
- Finance and investment analysis (SEC filings, earnings, contracts)
- Invoice processing (accounts payable automation)
- Insurance claims and underwriting
- HR / recruiting (resume parsing, ATS)
Recent updates
- LlamaParse API v2 (cleaner config, more structured outputs, new SDKs)
- LlamaSheets (Beta) for messy spreadsheets
- LlamaAgents Builder (NL-driven agent creation)
- Revamped n8n integration (nodes for LlamaParse/LlamaExtract)
Limitations
- Requires developer capability to get the most value
- Less drag-and-drop UI for non-technical teams
- Fast-moving ecosystem requires staying current
2. Reducto
Platform summary
Reducto is designed for high-fidelity extraction from visually complex documents, combining computer vision with VLMs.
Core features
- Layout-aware extraction (preserves spatial relationships)
- Figure & graph summarization (chart/graph → descriptive text)
- Agentic error correction (multi-pass OCR cleanup)
Primary use cases
- Investor decks & financial presentations
- Medical records parsing
- Legal document formatting
Recent updates
- Series B funding to enhance graph-to-text
- Improved multi-page table reconstruction
Limitations
- Extraction layer focus; needs downstream indexing tooling
- Enterprise pricing can be steep
- Smaller community/docs than some alternatives
3. Google Document AI
Platform summary
Serverless document processing in GCP with pre-trained and custom models.
Core features
- Pre-trained processors (invoices, tax forms, IDs, etc.)
- Human-in-the-loop validation
- Knowledge graph integration
Use cases
- Procurement automation
- Government form digitization
- Large-scale content classification
Recent updates
- Easier custom model training in Workbench
- Reduced labeled-data requirements in some cases
Limitations
- Strong dependency on Google Cloud
- Pricing/complexity can grow at scale
- Custom training still takes time + data
4. Amazon Textract
Platform summary
Managed AWS service for forms and tables, extending beyond plain OCR.
Core features
- Forms + table extraction (label/value relationships)
- Query-based extraction (ask for fields in natural language)
- Handwriting recognition
Use cases
- Mortgage & loan automation
- Healthcare intake
- Tax document processing
Recent updates
- Enhanced Analyze Lending coverage
- Improved query latency
Limitations
- Can be brittle with highly non-standard layouts
- Often needs post-processing
- Costs can rise with complex query usage
5. ABBYY
Platform summary
Long-time IDP leader with OCR depth plus enterprise workflow tooling (ABBYY Vantage).
Core features
- OCR skill library (pre-built “skills”)
- NLP-driven extraction
- Low-code orchestration
Use cases
- Logistics & supply chain
- Banking compliance (KYC/AML)
- Insurance underwriting
Recent updates
- Generative AI assistants for rule writing + summarization
Limitations
- Can feel heavyweight for small API-first teams
- Less flexible than some cloud-native parsers
- Licensing may not fit all dev-first orgs
6. Unstructured
Platform summary
Developer tooling to partition and normalize unstructured content for RAG and indexing.
Core features
- Document partitioning into consistent elements
- Metadata extraction (page numbers, hierarchy, etc.)
- Broad file type support (20+)
Use cases
- RAG pipeline ingestion
- Data lake transformation
- Content migration
Recent updates
- “Unstructured Serverless” managed API
- Enhanced vision-based table extraction
Limitations
- OSS can be compute-intensive
- More assembly required than “all-in-one” suites
- Accuracy varies on very complex visuals
7. UiPath
Platform summary
RPA leader with Document Understanding for extract + act automation.
Core features
- Hybrid extraction (rules/templates/ML)
- Drag-and-drop workflow designer
- Validation station for human review + retraining
Use cases
- Accounts payable automation
- HR onboarding
- Customer support workflows
Recent updates
- “Autopilot” gen-AI assistant for workflow creation
Limitations
- Best if you already use UiPath
- Overkill for extraction-only scenarios
- Requires UiPath-specific skills
8. Hyperscience
Platform summary
Enterprise-grade extraction for messy scans and handwriting, often used in government/regulated environments.
Core features
- Models tuned for handwriting/distortions
- Automated quality control + routing
- Flexible deployment (on-prem/private cloud/SaaS)
Use cases
- Government benefits administration
- Archive digitization
- Claims triage
Recent updates
- Hypercell for secure on-prem AI infrastructure
Limitations
- High entry cost for SMBs
- Longer implementations
- Most optimized for structured/semi-structured forms
9. Landing AI
Platform summary
Computer-vision-first document intelligence (especially when visual detail matters).
Core features
- Visual element identification (pixel-level)
- Data-centric labeling tools
- High-resolution document processing
Use cases
- Engineering drawings
- Retail label verification
- Geospatial document analysis
Recent updates
- Expanded Large Vision Model for zero-shot extraction
Limitations
- More visual than semantic/language-driven
- Requires integration work for LLM workflows
- Overkill for straightforward text PDFs
10. Extend
Platform summary
AI-native platform for turning documents into workflows and structured APIs.
Core features
- Agentic workflow engine
- LLM-native parsing (e.g., GPT-4/Claude-class models)
- Instant structured API generation for new doc types
Use cases
- Credit underwriting
- SaaS onboarding
- Vendor risk management
Recent updates
- Extend Studio (low-code agent builder)
Limitations
- Newer vendor with less long-term enterprise history
- Higher per-document costs (LLM-driven)
- Requires process automation mindset
Conclusion
Document extraction in 2026 is defined by agentic, context-aware AI, replacing brittle template OCR. The best choice depends on your stack:
- Developer-first + semantic extraction: LlamaParse
- Highly visual content: Reducto or Landing AI
- Cloud-native enterprise processing: Google Document AI / Textract
- Workflow/RPA automation: UiPath / Extend
- High-accuracy regulated forms: Hyperscience / ABBYY
FAQ
What is document extraction software?
Document extraction software automatically identifies and extracts data from documents (PDFs, scans, images). Beyond OCR, it uses AI/ML/NLP to understand structure and context—e.g., distinguishing billing vs shipping address or locating effective dates in contracts—so outputs become structured, actionable data.
Why is document extraction important?
Manual entry is slow, expensive, and error-prone. Automated extraction:
- Cuts processing time from days to seconds
- Improves accuracy and compliance
- Accelerates workflows like AP, onboarding, and claims
- Unlocks insights from “dark data” in PDFs/scans
How to choose the best provider
Evaluate:
1. Accuracy on your documents (do a POC/trial on real samples)
2. Integration (APIs, SDKs, connectors to ERP/CRM/data systems)
3. Scalability + usability (current/future volume, doc variety, ops experience)
Legacy OCR vs agentic document extraction
- Legacy OCR: pixel/template-based; breaks with layout/font changes
- Agentic extraction: LLM/VLM-based; understands semantics/layout; handles complex/nested/multi-modal docs
Can modern tools handle handwriting or highly visual documents?
Yes—accuracy varies by tool and document quality.
- Handwriting: Textract, Hyperscience
- Charts/graphs/visuals: Reducto, Landing AI
Challenges at scale
- Document diversity (formats/layouts/languages)
- Quality control (confidence thresholds, HITL review)
- Integration to downstream systems/pipelines
- Compliance (privacy, audit trails)
- Cost management (especially LLM-based extraction)
Integrating into AI / RAG pipelines
Most platforms provide APIs/SDKs (Python/TypeScript). Typical flow:
1. Preprocess/partition documents
2. Extract structured fields (schema-based if possible)
3. Store outputs + metadata + traceability
4. Index for retrieval (RAG) or push into databases/workflows
If you want, I can also: (1) turn this into a tighter blog post with headings + TL;DRs, (2) convert the comparison into a “decision tree”, or (3) rewrite the table for a specific audience (finance, insurance, legal, etc.).
