Enterprise Document Processing Platform
Purpose-built AI platform for intelligent document extraction, validation, and automation
DocuAgent combines Vision Language Model (VLM) technology with enterprise-grade architecture to automate document processing at scale.
Core Capabilities
Vision Language Model
Understands document content, not just text
- Handles complex layouts, handwriting, signatures
- Works across multiple languages and formats
- Superior to traditional OCR for structured data
Conversational Config Agent
Set up document extraction without coding
- Guided AI-driven configuration workflow
- Real-time feedback and suggestions
- Reduces setup time from hours to minutes
Intelligent Validation
Multi-document cross-validation and error detection
- Template-free adaptive processing
- Document-type-specific post-processing
- Error detection and correction workflow
Use Cases
Invoice Processing
Multi-supplier invoice processing with varying formats, automatic field extraction, multi-currency support, and compliance validation.
- • Vendor, amount, date, line items
- • Multi-currency support
- • Compliance validation
Supply Chain Documentation
Bills of lading, packing lists, purchase orders with different vendor formats, multi-language documents, and related document validation.
- • Bills of lading & packing lists
- • Multi-language support
- • Shipment tracking integration
Compliance & Audit
Extract and validate documents for regulatory compliance with signature detection, field completeness checking, and audit trails.
- • Signature detection & validation
- • Audit trail generation
- • Error correction workflow
Why DocuAgent
Enterprise-Grade Architecture
Built on NestJS microservices, real-time streaming, Azure cloud integration, and multi-tenant capable.
AI-First Approach
Vision Language Model understands context and relationships, adapts to document variations without templates.
No Templates Required
Understands content structure automatically, adapts to vendor variations, handles new formats without reconfiguration.
Intelligent Validation
Multi-document cross-checks, business rule validation, anomaly detection, and confidence scoring for each extraction.
Integration Options
REST API
Most common integration method
Standard HTTP endpoints with JSON responses. Works with any programming language.
POST /v1/execution/sdu-extractionStreaming API
Real-time feedback
Get partial results during processing, real-time progress updates for long documents.
Transfer-Encoding: chunkedEnterprise Features
Security & Compliance
- • Encryption at rest and in transit
- • Document retention policies
- • Secure API authentication (OAuth 2.0, JWT)
- • Audit logging
- • SOC 2 architectural design
- • GDPR/CCPA data handling
Scalability
- • Microservices architecture (independent scaling)
- • Batch processing (queue-based)
- • Stream processing (real-time)
- • Database optimization for large volumes
Deployment Models
- • Cloud Hosted (Enterprise, multi-tenant)
- • On-Premise (Sensitive data, air-gapped)
- • Hybrid (Flexible compliance)
Reliability
- • 99.9% uptime target (production)
- • Automatic failover
- • Database backups & recovery
- • Monitoring & alerting
Real User Stories & Business Scenarios
Real stories showing how Document Agent solves critical business problems in production environments.
User Story #1: Complete Order Processing Flow
Document Processing Manager | Order: A8ekdemo002
The Challenge
Processing hundreds of orders monthly. Each order requires Purchase Order, Commercial Invoice, and Delivery Document. Manual process takes 4-6 hours per order with 15-20% error rate.
- • Document collection from multiple sources (email, WhatsApp, portal)
- • Manual type validation: 15-20 minutes per document
- • Manual data extraction: 30-45 minutes per document, 15-20% error rate
- • Cross-document validation: 1-2 hours per order, easy to miss discrepancies
The Solution
3 documents validated automatically in 2-3 minutes (vs. 45-60 minutes manually)
All fields extracted from 3 documents in 5-10 minutes (vs. 90-135 minutes manually)
13 business rules checked automatically in 3-5 minutes (vs. 1-2 hours manually)
Business Impact
User Story #2: Checker Catches Critical Discrepancies
Compliance Officer | Order: A8ekdemo002
The Challenge
Documents appear correct individually, but contain inconsistencies when compared. Manual cross-checking takes 1-2 hours and misses 30-40% of discrepancies.
How Checker Solved This
Business Impact
- • Payment to wrong order: $5K-$50K per incident
- • Shipment to wrong location: $10K-$100K per incident
- • Compliance violations: $10K-$100K per violation
Real Business Scenarios
The Misrouted Shipment
8 real cases in production
Purchase Order shows delivery to New York, but Delivery Document shows Los Angeles. Without Checker, shipment goes to wrong location.
The Payment Mix-Up
5 real cases in production
Invoice shows different PO number than Purchase Order. Without Checker, payment goes to wrong supplier or order.
The Quantity Discrepancy
5 real cases in production
Invoice shows 1,100 units but only 1,000 delivered. Without Checker, company overpays by $10K.
Complex Document Processing
Proven at scale
System handles high-complexity scenarios: 115 fields from single document, 396 fields across 13 documents, 17-row tables.
Competitive Comparison
| Feature | DocuAgent | Traditional OCR | Template-Based | Generic AI |
|---|---|---|---|---|
| Setup Time | ||||
| Template Required | ||||
| Multi-Language | ||||
| Complex Layouts | ||||
| Validation & Post-Processing | ||||
| Conversational Setup |