Multimodal AI · Document Intelligence · Finance & Legal
GPT-4o Vision · LlamaParse · AWS Textract · FastAPI
Multimodal Document
Intelligence Pipeline
End-to-end AI pipeline that turns unstructured documents — invoices, contracts, purchase orders, medical records — into validated structured data at scale. GPT-4o Vision extracts structured JSON, validated against ERP/PO databases, with human-review routed only for low-confidence extractions. 90%+ accuracy at less than $0.05 per document.
Results
90%+
Extraction accuracy straight from AI
99%+
Accuracy after human-review of low-confidence docs
<$0.05
Per document — vs ~$3.50 manual processing
70×
Cost reduction vs manual data entry at scale
Document Types Handled
The pipeline is multimodal — it handles typed text, handwritten notes, scanned images, mixed-format PDFs, and documents with tables, stamps, and signatures. No preprocessing required.
🧾
Invoices
Line items, totals, tax
📝
Contracts
Clauses, parties, dates
📦
Purchase Orders
SKUs, quantities, pricing
🏥
Medical Records
Diagnoses, medications
🏦
Bank Statements
Transactions, balances
📋
Forms & Applications
Fields, signatures
The Problem
Manual document processing is one of the highest-cost, lowest-value activities in finance, legal, and operations. At $3–4 per document and error rates of 2–5%, it doesn't scale — and the people doing it hate it.
The client was processing thousands of invoices and purchase orders per month across multiple suppliers, each with different formats, layouts, and languages. They needed a system that could ingest any document format, extract structured data accurately, validate it against their ERP, and flag only the exceptions for human review.
Key challenges
- Hundreds of different document formats and layouts
- Mix of digital PDFs, scanned images, and handwritten docs
- Extraction needed to match ERP field schema exactly
- Low-confidence extractions needed human routing — not all-or-nothing
- Multi-language documents (English, French, German)
- Audit trail required for every extraction decision
Our solution
- GPT-4o Vision — multimodal extraction from any document format
- LlamaParse — structured PDF parsing with table and layout awareness
- AWS Textract — OCR layer for scanned and handwritten documents
- Confidence scoring — routes low-confidence extractions to human review
- ERP/PO validation — cross-checks extracted data against source records
- Full audit trail — every extraction logged with confidence score and model version
ROI at Scale
The economics of AI document processing improve dramatically at scale. Payback typically happens within weeks of deployment.
$3.50
Manual cost per document
<$0.05
AI cost per document
$34,500
Saved per 10,000 documents
Weeks
Typical payback period
Architecture
Document to validated structured data — full pipeline
📄
Document In
PDF / image / scan
→
🔍
OCR + Parse
Textract + LlamaParse
→
🤖
GPT-4o Vision
Extract JSON
→
📊
Confidence Score
High / low
→
✅
ERP Validation
Match & approve
→
👤
Human Review
Low-confidence only
System components
1
Document Ingestion
AWS S3PDF · Image · ScanMulti-languageBatch + real-time
2
OCR & Parsing
AWS TextractLlamaParseTable extractionLayout awareness
3
AI Extraction
GPT-4o VisionStructured JSONSchema-constrained outputConfidence scoring
4
Validation Layer
ERP / PO cross-checkBusiness rules engineAuto-approve high-confidence
5
Human Review Queue
Low-confidence routingReview UIFeedback loop → model improvement
6
API & Audit Trail
FastAPIPostgreSQLFull audit logModel version tracking
GPT-4o VisionLlamaParseLangChain
AWS TextractAWS S3FastAPI
PostgreSQLDockerStructured JSONERP Integration