You will get AI document processing, OCR MCP and RAG system
Project details
I will build a custom AI document intelligence system that can process PDFs, scanned documents, and other files using OCR, LLMs, semantic search, and RAG.
The solution can automatically extract structured information, process large document collections incrementally, generate reports, and provide citation-based AI answers from your documents.
I can build the system using **Python, FastAPI, OCR, OpenAI or open-source/local LLMs, Qdrant, vector search, hybrid RAG, and secure document storage**, with architecture tailored to your data, workflow, and deployment requirements.
Ideal for legal, healthcare, insurance, compliance, research, and other businesses that need to turn large document collections into **searchable, structured, and actionable information**.
The solution can automatically extract structured information, process large document collections incrementally, generate reports, and provide citation-based AI answers from your documents.
I can build the system using **Python, FastAPI, OCR, OpenAI or open-source/local LLMs, Qdrant, vector search, hybrid RAG, and secure document storage**, with architecture tailored to your data, workflow, and deployment requirements.
Ideal for legal, healthcare, insurance, compliance, research, and other businesses that need to turn large document collections into **searchable, structured, and actionable information**.
AI Algorithms
Autoencoder, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AIOps, Anomaly Detection, Conversational AI, Image Processing, Machine Translation, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, PyTorch, TensorFlow, Word2vecAI Models
GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,500
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 3 | 0 |
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 | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$200 - $1,500About Hamza
Senior Generative AI Engineer | RAG, LLMs & AI Agents
Lahore, Pakistan - 5:11 am local time
With 5+ years in AI/ML engineering, I specialize in RAG applications, LLM agents, multimodal search, tool calling, LLM evaluation, and real-time voice AI. I help companies move from AI idea or prototype to a scalable, usable solution.
Recent work includes:
• Enterprise multimodal RAG platform for retrieving information across documents, files, videos, and images
• AI legal and real-estate agent using RAG and vector databases for clause-level retrieval and automated contract drafting, reducing drafting costs by up to 80%
• Real-time AI voice ordering assistant using Silero VAD, Whisper ASR, LLaMA-based models, and function calling
• Computer-vision solution for KFC drive-through operations that reduced customer waiting time by 20%
• Generative AI platform that converts text prompts into interactive games, stories, and animations
My core stack includes Python, PyTorch, LangChain, LangGraph, LlamaIndex, MCP, RAG, vector databases (FAISS, Pinecone, Chroma), open-source LLMs, vLLM, Ollama, Docker, microservices, REST APIs, and LLM evaluation.
I can help with:
• RAG chatbot or internal knowledge assistant
• AI agents with tools, workflows, and integrations
• Document intelligence and multimodal search
• LLM evaluation, optimization, fine-tuning, and deployment
• Voice AI assistants
• AI architecture and technical consulting
Send me your use case, data sources, and desired outcome—I’ll recommend a practical architecture and clear next steps.
Steps for completing your project
After purchasing the project, send requirements so Hamza can start the project.
Delivery time starts when Hamza receives requirements from you.
Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
OCR
Ocr for the provided documents
Extraction
What need to extract from the Documents