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Architecture Reference Blueprint

Automated Document Intelligence & OCR Extraction Pipeline

Transform millions of unstructured PDFs, scans, and invoices into clean, validated relational data using hybrid OCR and LLM extraction.

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System Constraints

Non-Negotiable Architecture Constraints

✓Extract structured data from complex multi-page invoices with > 99% accuracy
✓Process 100-page financial PDF filings in under 15 seconds
✓Automated confidence scoring routing low-confidence fields to human reviewers
✓PII and sensitive financial data redacted prior to cloud model ingestion

Component Topology

System Components & Technologies

Modular subsystems designed with decoupled responsibilities, clear contracts, and scalable storage layers.

3D Isometric Architecture

Automated Document Intelligence & OCR Extraction Pipeline Stack Topology

Live Telemetry Active
Tier 1: DocumentTier 2: OCRTier 3: LLMTier 4: Human
01

Document Ingestion Gateway

< 15ms
Role: Handling PDF uploads, rasterization, and virus scanningFastAPI + AWS S3
02

OCR & Layout Engine

< 35ms
Role: Extracting raw text coordinates and bounding boxes from scanned imagesAmazon Textract / Google Document AI
03

LLM Extraction & Schema Normalizer

< 5ms
Role: Mapping extracted text into strict Pydantic JSON schemasAnthropic Claude 3.5 Sonnet
04

Human Verification Queue

< 1ms
Role: Side-by-side document review UI with bounding box highlight overlaysNext.js + PDF.js
Subsystem 01

Document Ingestion Gateway

Handling PDF uploads, rasterization, and virus scanning

Production Stack:

FastAPI + AWS S3

Subsystem 02

OCR & Layout Engine

Extracting raw text coordinates and bounding boxes from scanned images

Production Stack:

Amazon Textract / Google Document AI

Subsystem 03

LLM Extraction & Schema Normalizer

Mapping extracted text into strict Pydantic JSON schemas

Production Stack:

Anthropic Claude 3.5 Sonnet

Subsystem 04

Human Verification Queue

Side-by-side document review UI with bounding box highlight overlays

Production Stack:

Next.js + PDF.js

Data Lifecycle

End-to-End Data Flow Sequence

1

User or automated email bot uploads multi-page invoice PDF to secure S3 bucket.

2

Lambda dispatcher triggers Amazon Textract to generate text coordinate blocks and table arrays.

3

Layout-aware prompt builder injects OCR text into Claude 3.5 Sonnet with a strict Pydantic JSON schema.

4

LLM returns validated structured JSON (vendor, tax ID, line items, totals) with field confidence scores.

5

If confidence > 95%, data syncs directly to ERP (NetSuite); otherwise routes to Next.js human verification queue.

Reliability & Resilience

Failure modes & automated mitigations

Failure Mode 01

Malformed Hand-Drawn or Blurry Scans

Mitigation Architecture

Apply image preprocessing (deskewing, contrast enhancement, binarization) before running OCR.

Failure Mode 02

Hallucinated Line Item Numbers

Mitigation Architecture

Implement deterministic mathematical validation rules (sum of line items must exactly equal total amount) to flag discrepancies.

Failure Mode 03

Token Limit Exceeded on 500-Page Documents

Mitigation Architecture

Chunk documents by logical page sections, running parallel extraction per section before merging.

Architecture FAQs

Frequently asked blueprint questions

Template OCR breaks whenever a vendor changes their invoice layout. LLMs understand document semantics, extracting correct fields regardless of layout variations.

Our Next.js review interface displays the PDF side-by-side with extracted fields, highlighting exact bounding boxes on the original scan for rapid 5-second operator verification.

Senior engineering teams that build for long-term production health

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