You will get Enterprise RAG Infrastructure for Complex Document Corpora
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Top Rated
Project details
Want to build your own AI-powered app with Langchain? Imagine having a 500-page document that would typically require 24+ hours of reading and searching to find the specific information you desire. However, with the aid of a chatbot, you can accomplish this task in no time at all. I specialize in creating user-friendly web applications equipped with AI chatbots that harness advanced natural language processing and embeddings.
Data Sources:
• TXT
• PDF
• PPT
• Docx
• Excel
Others
What kind of application can be built with it?
• Document Summarization
• Data Scraping and Q&A with any URL
• Q&A with Documents
• Q&A with pdf,csv, excel, ppt
Tech used:
• Python
• Streamlit
• OpenAI
• Gemini pro
• Langchain
Please feel free to inquire about my services. I'll be glad to help with no obligations or pressure until you are comfortable.
Data Sources:
• TXT
• PPT
• Docx
• Excel
Others
What kind of application can be built with it?
• Document Summarization
• Data Scraping and Q&A with any URL
• Q&A with Documents
• Q&A with pdf,csv, excel, ppt
Tech used:
• Python
• Streamlit
• OpenAI
• Gemini pro
• Langchain
Please feel free to inquire about my services. I'll be glad to help with no obligations or pressure until you are comfortable.
AI Algorithms
Convolutional Neural Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, Image Processing, Natural Language Understanding, Object Detection, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LaMDA, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$180
|
Standard
$450
|
Advanced
$950
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 14 days |
Number of Revisions | 180 | 450 | 950 |
AI Model Integration | |||
Batch Normalization | - | - | |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | ||
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | |||
Source Code |
21 reviews
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MN
Madhukar N.
Apr 15, 2026
OCR Automation for Faxes with AI
good experience
TJ
Thies J.
Jan 12, 2026
Backend Engineer
SG
Snehashis G.
Dec 16, 2025
Consultation
Krupali is great, very responsible, have the best professional mind and i will work with her again.
JG
JP G.
Nov 17, 2025
Markdown File Data Extraction Specialist
Incredibly accurate & proactive. As engineer since 35 years , I was rarely faced to such mindset & efficiency. Thanks so much !
AS
Atul S.
Sep 30, 2025
RAG Consultant for Implementation Needs for 1 hour
I couldn't be happier with the work delivered on this project! From the very beginning, it was clear that Krupali really knew her stuff when it came to RAG pipeline architecture, retrieval strategies, and LLM integration. Even though this was just a one-hour brainstorming session, she packed so much value into that time. The quality of insights was outstanding - she quickly understood our system's requirements and provided accurate, actionable recommendations that were exactly what we needed. What really impressed me was how she came prepared and made every minute count, delivering clarity on complex RAG implementation challenges like chunking strategies, embedding models, vector database selection, and retrieval optimization way faster than I expected. She didn't just answer my questions - she actually went above and beyond by suggesting ways to improve our document processing pipeline, identifying potential issues with context relevance, and even sharing best practices for improving accuracy in our specific use case. Her deep expertise in generative AI, RAG workflows, OCR integration, and document processing really shone through. Communication was crystal clear throughout, and she explained technical concepts in a way that was easy to understand while still being thorough. It's rare to find someone who combines such strong technical knowledge in LLMs and RAG pipelines with the ability to think strategically about real-world implementation. I'm definitely planning to work with Krupali again on future AI projects, and I'd highly recommend her to anyone looking for expert guidance on RAG systems or AI solutions. Thanks again for the excellent consultation!
About Krupali
AI Data Extraction & RAG Engineer | Intelligent OCR & Document AI
100%
Job Success
Surat, India - 6:04 am local time
If you are dealing with standard vector-search chatbots that hallucinate, lose context, or fail to read dense data tables, or if your legacy OCR tools are outputting broken, chaotic text from blurry scans, faxes, and invoices, I build the automated bridge to clean, database-ready structured data.
Unlike generalist script-writers who blindly dump data into basic vector stores, I design enterprise-ready, layout-aware, cost-optimized pipelines engineered specifically for highly confidential, unstructured, multi-column, and multi-page corporate assets.
CORE WORKFLOWS & SEARCH-OPTIMIZED ARCHITECTURES:
1. Production RAG Pipelines & Enterprise Knowledge Bases
• Advanced Hybrid Search Indexing: Fusing dense semantic vector embeddings (Pinecone, Qdrant, ChromaDB) with sparse keyword retrieval (BM25) to guarantee specific alphanumeric codes, legal sections, and invoice serial numbers are never missed during a query.
• Two-Stage Context Reranking: Implementing Cross-Encoder Rerankers (Cohere Rerank, FlashRank) to filter retrieved document nodes down to the absolute best context chunks, slashing downstream LLM token costs by up to 40% while accelerating system execution speed.
• Layout-Aware Hierarchical Chunking: Parsing structured document layouts natively to maintain strict parent-child context windows, preventing sentences or complex financial data tables from being blindly split in half during vector indexing.
2. Intelligent AI OCR & Multi-Modal Document Intelligence
• Vision-Based Pre-Processing: Utilizing OpenCV, PaddleOCR, and LayoutLMv3 to automatically de-skew, binarize, and map out the multi-column reading order of messy scanned PDFs, faxes, or smartphone images before running extraction.
• Multi-Modal LLM & Vision Data Extraction: Routing raw text tokens and visual patches through state-of-the-art vision-language models (GPT-4o, Claude 3.5 Sonnet, or local open-source models like Llama-3.2-Vision and Qwen2.5-VL) to automatically repair transcription typos and misaligned characters by understanding the surrounding industry context.
3. Schema Enforcement & Human-in-the-Loop (HITL) Guardrails
• Strict Schema Enforcement: Employing programmatic validation frameworks like Instructor and Pydantic to mathematically force LLM outputs into 100% compliant, database-ready JSON, CSV, or SQL structures at the native API level.
• Algorithmic Triage: Tracking model confidence log-probabilities. High-confidence data saves instantly to production databases, while low-confidence edge cases route seamlessly to custom Streamlit dashboards for rapid human validation.
PROVEN ENTERPRISE CASE STUDIES & SUCCESS METRICS:
• Enterprise Document Intelligence: Multimodal AI OCR, Pydantic Extraction & Hybrid RAG Pipeline
Designed and engineered an end-to-end production pipeline to automate the processing of dense, unstructured corporate assets (including scanned PDFs, financial statements, and high-volume faxes). Used LayoutLMv3 to preserve structural hierarchies and the Instructor library paired with strict Pydantic schemas to achieve an elite 96.8% verified field-level extraction accuracy across 10,000+ pages.
• AI OCR Automation for High-Volume Faxes: Engineered a highly resilient extraction and contextual text-cleaning pipeline running flawlessly for complex, handwritten and blurry business records.
• Markdown Data Extraction Specialist: Built a hyper-accurate pipeline converting complex document styles into structured markdown format for seamless vector indexing and RAG ingestion
• Enterprise Document Parsing: Consulted on and deployed custom backend AI microservices to clean, parse, and automate unstructured multi-page document flows.
MODERN PRODUCTION TECH STACK:
• LLMs & Vision-Language Models: GPT-4o, Claude 3.5 Sonnet, Llama-3.2-Vision, Qwen2.5-VL, DeepSeek-VL2
• Frameworks & Validation: Instructor (Pydantic), LangChain, LlamaIndex, vLLM, Ollama, LangGraph
• OCR & Computer Vision: Tesseract OCR, EasyOCR, PaddleOCR, OpenCV, LayoutLMv3, Marker, Text Extraction
• Infrastructure & Vector DBs: Python, AI Chatbot Development, VectorDBs (Pinecone, Chroma, Qdrant), PostgreSQL, Docker, Data Scraping, Web Scraping, PDF to Excel
Ready to eliminate hallucinations, protect your data privacy, and automate your critical document workflows with deterministic accuracy? Click "Invite to Job" to review your current pipeline architecture.
Steps for completing your project
After purchasing the project, send requirements so Krupali can start the project.
Delivery time starts when Krupali receives requirements from you.
Krupali works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis
Review the client's requirements, including their choice of AI model (GPT-4 or Gemini Pro), API keys, and any customization needs.
Environment Setup
Set up the development environment for Streamlit and configure API access for the chosen model.