HS
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Enterprise AI/ML System for Automated Document Processing & Analysis
Architected and developed enterprise-grade AI/ML platform for intelligent document processing, classification, and extraction using advanced NLP and Computer Vision techniques. Implemented GPT-4 integration, custom ML models, and automated workflows processing 100K+ documents monthly with 95% accuracy, reducing manual processing time by 80%.
Enterprise organization struggled with massive volumes of unstructured documents (invoices, contracts, forms, reports) requiring manual data extraction and classification. Existing OCR solutions had poor accuracy with complex layouts, handwritten text, and multi-language documents. Manual processing created bottlenecks, high costs, and human errors while hindering scalability and decision-making speed.
Built hybrid AI system combining GPT-4 API, custom TensorFlow models, and traditional ML techniques for document classification, entity extraction, and semantic analysis
Developed Computer Vision pipeline using OpenCV and PyTorch for layout analysis, table detection, signature verification, and handwriting recognition
Implemented intelligent document routing with 15+ specialized classification models achieving 95% accuracy across invoice, contract, form, and report categories
Created real-time extraction pipeline using Named Entity Recognition (NER), Regex patterns, and GPT-4 for structured data extraction from unstructured documents
Built validation engine with confidence scoring, human-in-the-loop feedback system, and continuous model retraining from user corrections
Balancing high-accuracy deep learning models (slow inference) with real-time processing requirements for enterprise scale (100K+ documents/month).
Implemented multi-tier architecture with fast classification models routing to specialized extraction models only when needed. Used model quantization, TensorRT optimization, and batch processing to achieve <2s latency. Deployed GPU-accelerated inference servers with auto-scaling for peak loads.
Documents varied wildly—clean PDFs, scanned images, handwritten forms, multi-language contracts, complex tables. Traditional OCR failed on 40% of documents.
Built preprocessing pipeline with image enhancement (denoising, deskewing, contrast adjustment), multi-OCR engine fallback (Tesseract, Google Vision, AWS Textract), and GPT-4 post-processing for error correction. Implemented document quality scoring to route poor-quality scans to human reviewers.
Initial models needed improvement from real-world usage. Manual model retraining was time-consuming and required ML expertise.
Built human-in-the-loop feedback system capturing user corrections as training data. Implemented automated retraining pipeline with MLflow tracking, A/B testing for new model versions, and gradual rollout based on performance metrics. Created annotation interface for data labeling by non-technical staff.
Learned that combining GPT-4, custom ML models, and rule-based systems yields better results than any single approach. GPT-4 excels at semantic understanding but is expensive and slow; custom models offer speed and cost efficiency for specific tasks. Hybrid architecture optimizes for accuracy, speed, and cost.
Discovered that ML model development is only 20% of AI product success—the other 80% is infrastructure: versioning, monitoring, retraining, A/B testing, feedback loops. Built comprehensive MLOps pipeline with experiment tracking, model registry, automated testing, and performance monitoring.
Enterprises demand transparency in AI decisions for compliance and trust. Implemented confidence scores, decision explanations, audit trails, and human review workflows. Explainable AI features were crucial for stakeholder buy-in and production adoption.
Direct government application - intelligent document processing for permits, applications, contracts, invoices, compliance reports, FOIA requests, and citizen correspondence across federal, provincial, and municipal agencies
"This AI platform transformed our document processing operations. What used to take hours now takes minutes with 95% accuracy. The hybrid approach combining GPT-4 with custom models gave us the best of both worlds—intelligence and cost-efficiency. The human-in-the-loop design ensures we maintain control while leveraging AI automation. Exceptional work."