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Azim BaigTech Architect
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AI / OCR / AutomationAI Automation

AI-Powered Intelligent Document Processing & OCR Engine

Designed an end-to-end intelligent document intelligence pipeline turning messy scanned PDFs, invoices, and contracts into verified structured JSON data with automated validation loops.

85%Manual Effort Reduction
98.4%Extraction Accuracy
1.8s/docProcessing Latency
PythonOCR EngineLLM IntegrationFastAPIDocument AIPostgreSQLAsync Workers
Architectural Highlights & Key Innovations
Multi-stage async processing queue with Celery/Redis
Hybrid OCR + LLM contextual extraction for irregular layouts
Human-in-the-loop fallback for low-confidence threshold exceptions

Core Technical Scope

Multi-format ingestionNoise Reduction & OCRDocument ClassificationField & Table ExtractionConfidence Scoring & ValidationDownstream API Webhooks

Executive Challenge

Organizations spend thousands of manual human hours transcribing scanned receipts, bills of lading, and multi-page agreements into database systems, leading to high latency and transcription errors.

Architectural Strategy

Constructed a resilient multi-stage processing pipeline: Document Ingestion $\rightarrow$ Pre-processing & OCR $\rightarrow$ Semantic Classification $\rightarrow$ LLM-Assisted Entity Extraction $\rightarrow$ Rule-Based Schema Validation $\rightarrow$ Downstream ERP/CRM Sync.

  • Intelligent layout segmentation capable of parsing multi-column tables
  • Dynamic schema mapping that adapts to varied vendor invoice formats
  • Confidence scoring module triggering automated human verification alerts only when needed

Engineered Solution

A high-throughput API service capable of processing thousands of complex documents daily, providing real-time webhooks and structured audit logs.

Technical Stack & Delivery

Python 3.11, FastAPI, Advanced OCR engines, Vision-Language Models, Redis async job queues, Dockerized microservice architecture.

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