You will get I'll build a RAG chatbot on your business documents
Project details
I build production-grade RAG chatbots that actually work — not demos, but real systems you can deploy and use immediately.
My recent work: 95% extraction accuracy on 500+ docs/hour, 70% LLM token cost reduction, 99.5% uptime on financial data pipelines. I've built RAG systems for energy data across 15+ countries and financial document extraction for 50+ reports.
What you get: clean Python code, full setup instructions, source citations on every answer, and a chatbot that understands YOUR documents — not generic responses.
Stack: LangChain, GPT-4o, Gemini, Pydantic, AWS S3, FAISS/ChromaDB.
Fixed scope. Fast delivery. Production-ready.
My recent work: 95% extraction accuracy on 500+ docs/hour, 70% LLM token cost reduction, 99.5% uptime on financial data pipelines. I've built RAG systems for energy data across 15+ countries and financial document extraction for 50+ reports.
What you get: clean Python code, full setup instructions, source citations on every answer, and a chatbot that understands YOUR documents — not generic responses.
Stack: LangChain, GPT-4o, Gemini, Pydantic, AWS S3, FAISS/ChromaDB.
Fixed scope. Fast delivery. Production-ready.
AI Algorithms
AdaBoost, AlexNet, Autoencoder, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
GitHub Copilot, Gradio, Hugging Face, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$150
|
Standard
$250
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
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.
Streamlit UI for the chatbot
(+ 2 Days)
+$50Frequently asked questions
About Gangatharan
GenAI Engineer - RAG, LangGraph, Multi-Agent Systems, LLM Pipelines
Bengaluru, India - 9:39 am local time
Recent results: 95% extraction accuracy on 500+ scanned docs/hour | 70% LLM token cost reduction | 60% runtime improvement via async batching | 99.5% uptime on financial data pipelines.
What I build for clients:
→ RAG chatbots on your documents (PDFs, scanned files, reports)
→ Multi-agent pipelines using LangGraph for data extraction & validation
→ MLOps infrastructure (MLflow, CI/CD, Docker, Kubernetes on AWS)
→ OCR + LLM pipelines for scanned/unstructured documents
Stack: LangChain, LangGraph, Gemini, GPT-4o, AWS, Docker, Kubernetes, PyTorch, Pydantic.
Fixed-price projects available. Fast delivery. Production-ready code.
Steps for completing your project
After purchasing the project, send requirements so Gangatharan can start the project.
Delivery time starts when Gangatharan receives requirements from you.
Gangatharan works on your project following the steps below.
Revisions may occur after the delivery date.
Document Processing
Load, chunk and embed your PDFs into a vector store with optimized retrieval settings.
RAG Pipeline Build
Build LangChain RAG pipeline with GPT-4o, prompt engineering and source citation.