I will build a rag application using langchain and llms

I build RAG (Retrieval-Augmented Generation) applications using LangChain and LLMs that connect your PDFs, databases, or websites to an AI that actually knows your content.

No more hallucinations. No more generic answers. Your AI, trained on your data.

What I build:

RAG pipelines that ingest PDFs, Word docs, URLs, databases

Vector search with FAISS, Pinecone, or ChromaDB

LangChain retrieval chains with source citations

LangSmith monitoring for tracing and evaluation

FastAPI backend + React frontend

AWS deployment (Lambda, S3, EC2)

Perfect for:

Companies wanting a Q&A bot over internal documents

Law firms needing contract analysis assistants

SaaS products adding AI search to their knowledge base

Researchers building literature review tools

Why RAG over fine-tuning?

RAG is faster, cheaper, and keeps your data up to date no retraining needed. You can update documents anytime and the AI stays current.

My stack:

LangChain · LangSmith · OpenAI / Claude · FAISS · Pinecone · FastAPI · React · AWS S3 / Lambda

Before ordering:

Message me with your document types and use case. I'll confirm the right approach and package for you within a few hours.

Let's make your data talk!