You will get Enterprise Document Intelligence System - RAG + LLM Pipeline
Project details
A production-grade Retrieval-Augmented Generation (RAG) pipeline for intelligent document extraction and Q&A.
The system processes large volumes of unstructured documents (PDFs, scanned forms, clinical notes, policy documents) and enables users to query them in natural language - returning accurate, cited answers using LLMs.
Core capabilities built:
Automated ingestion and chunking of multi-format documents (PDF, DOCX, images)
Semantic search using vector embeddings (OpenAI / HuggingFace)
RAG pipeline with LangChain + Pinecone / ChromaDB
Hallucination control via source grounding and confidence scoring
REST API layer (FastAPI) for integration with enterprise systems
Domain-specific prompt engineering for Insurance & Healthcare terminology
Business impact:
Reduced manual document review time by 70%+
Enabled non-technical users to query complex policy and clinical documents instantly
Deployed at enterprise scale handling thousands of documents per day
The system processes large volumes of unstructured documents (PDFs, scanned forms, clinical notes, policy documents) and enables users to query them in natural language - returning accurate, cited answers using LLMs.
Core capabilities built:
Automated ingestion and chunking of multi-format documents (PDF, DOCX, images)
Semantic search using vector embeddings (OpenAI / HuggingFace)
RAG pipeline with LangChain + Pinecone / ChromaDB
Hallucination control via source grounding and confidence scoring
REST API layer (FastAPI) for integration with enterprise systems
Domain-specific prompt engineering for Insurance & Healthcare terminology
Business impact:
Reduced manual document review time by 70%+
Enabled non-technical users to query complex policy and clinical documents instantly
Deployed at enterprise scale handling thousands of documents per day
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Machine Translation, Natural Language Generation, Natural Language Understanding, Object Detection, Sentiment Analysis, Sequence Modeling, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, PyTorchAI Models
BERT, GPT-4What's included $5,000
These options are included with the project scope.
$5,000
- Delivery Time 45 days
- Number of Revisions 2
- AI Model Integration
- Database Integration
- Model Deployment
- Model Documentation
- Model Monitoring
- Model Testing & Optimization
- Model Tuning
- Natural Language Processing
- Prompt Engineering
Optional add-ons
You can add these on the next page.
Fast 7 Days Delivery
+$1,000
Additional Revision
+$1,200About Sumeet
LLM Apps, RAG & Document Extraction Expert | GenAI Developer
Gurgaon, India - 3:54 pm local time
Most notably, I was part of the core team that built an intelligent document extraction and processing product powered by Large Language Models, used by Insurance and Healthcare enterprises on AWS cloud.
What I build for clients:
LLM-powered apps (OpenAI GPT-4/5, Claude, Gemini, Llama)
RAG pipelines (LangChain, LlamaIndex, vector DBs — Pinecone, Weaviate, ChromaDB)
Document intelligence & extraction systems (OCR + LLM)
AI agents and multi-step reasoning workflows
Fine-tuning & prompt engineering for domain-specific use cases
NLP pipelines for Insurance, Healthcare & Finance
Why clients hire me over other GenAI freelancers:
I've shipped a real enterprise AI product - not just tutorials or toy projects. I understand production concerns: latency, hallucination control, cost optimization, and scalability.
Available for both short-term builds and long-term AI development partnerships.
Steps for completing your project
After purchasing the project, send requirements so Sumeet can start the project.
Delivery time starts when Sumeet receives requirements from you.
Sumeet works on your project following the steps below.
Revisions may occur after the delivery date.
UAT
User acceptance testing, 60% payment to be completed