Krupali S.

I'm Krupali, an AI Engineer niche experties in AI OCR data extraction,AI Agent & RAG Architect who turns messy documents and unreliable chatbots into deterministic, production-grade saas end to end data workflows 5x-7x faster than manual entry, with zero hallucinated answers. If your OCR tools output broken text from blurry scans and invoices, or your RAG chatbot hallucinates and loses context on dense tables, I build the fix: layout-aware OCR pipelines and hybrid-search RAG architectures engineered for confidential, multi-page corporate documents not generic scripts dumping data into a basic vector store. CORE RESULTS 96.8% verified field-level extraction accuracy across 10,000+ pages processed End-to-end pipelines: PDF/Image to Excel, CSV, JSON, SQL, database-ready output 100% Job Success, Top Rated, 5 stars consistent, accurate deliveries still in active use by clients today KEY SERVICES - OCR & Document AI: layout-aware extraction from scanned PDFs, faxes, forms, invoices, multi-language and handwritten documents with python(Mistral OCR v3,dots.ocr,Tesseract, PaddleOCR, LayoutLMv3,Openai,Gemini, Clude,Azuer,DeepSeek OCR,olmOCR 2,Qwen3-VL-8B etc.) - LLM RAG & Enterprise Knowledge Bases: hybrid search (dense vector + BM25 keyword) with cross-encoder reranking, so answers are grounded and specific codes/numbers are never missed - Schema-Enforced Data Extraction: Instructor + Pydantic validation forcing LLM output into 100% compliant JSON/CSV/SQL, with human-in-the-loop review for low-confidence cases - AI Chatbots & Agents: LLM (local or api based) powered assistants for customer support and internal workflows - Computer Vision: image classification, object detection, automated visual inspection RECENT PROJECT TYPES - Multi-language PDF OCR pipeline (LayoutLMv3 + GPT-4o), 96.8% verified accuracy across 10,000+ pages - Self-initiated SaaS-style builds: RAG-based document Q&A platform, semantic search engine over embeddings, and a drop-in AI chat widget built to demonstrate production-grade architecture (LangChain, OpenAI, vector search) - OCR Automation for Faxes with AI completed project(gemini+paddle OCR)-end to end saas from scanned images to searchable Rag. - AI Consultation engagement turned into complete saas rag, client noted "the best professional mindset" and rehired - Markdown/document data extraction (Belgium client) expanded from an initial consultation into a full production RAG pipeline using Gemini Vision + PaddleOCR and OpenAI models over the client's own dataset, 95%+ accuracy, ~7x faster than manual processing - Hybrid RAG pipeline for enterprise knowledge base with reranking, cutting downstream LLM costs ~40% - High-volume fax OCR automation for handwritten/blurry business records - Scanned document to structured JSON/markdown pipeline for RAG ingestion - Visual QC system using computer vision for defect detection TECHNOLOGY STACK AI, LLM, Multimodal OpenAI, GPT all, Gemini, Claude, Llama, Qwen, DeepSeek, Generative AI, LLMs, Vision-Language Models, Multimodal AI, Prompt Engineering, Structured Outputs. OCR, Document AI Mistral OCR v3,dots.ocr,Tesseract, PaddleOCR, LayoutLMv3,Openai,Gemini, Clude,Azuer,DeepSeek OCR,olmOCR 2,Qwen3-VL-8B, EasyOCR, OpenCV, LayoutLM, LayoutLMv3, PDF Processing, Layout Analysis, Document Classification, Table Extraction, Handwritten Text Recognition. Computer Vision, ML OpenCV, PyTorch, TorchVision, Image Processing, Object Detection, Image Classification, Image Segmentation, Feature Extraction, Deep Learning, Machine Learning, Model Inference. RAG, Retrieval LangChain, LlamaIndex, LangGraph, Ollama, vLLM, Qdrant, Pinecone, FAISS, ChromaDB, Embeddings, Semantic Search, Hybrid Search, Reranking. Engineering Python, SQL, FastAPI, REST APIs, PostgreSQL, Pydantic, Instructor, Docker, Git, Linux. WHY CLIENTS HIRE ME I don’t just aim to make an AI system work once. I want to understand why it works, where it breaks, and how reliably it performs in the real world. Whether I’m working on OCR, Document AI, RAG, LLM applications, or AI agents, I approach problems through experiments, evaluation, error analysis, and measurable results not assumptions. I’ll show you what I tested, what the data revealed, what didn’t work, and what I changed because of it. So you’re not just getting a polished demo you’re getting a solution that has been tested, understood, and improved with evidence. My goal is simple: solve the problem properly, make the system measurable, and leave you with something you can actually trust. Tell me what documents or data you’re working with or where your chatbot is hallucinating. I’ll tell you exactly how I’d approach fixing it.