AI Agent and RAG Chatbot Development | LangChain and OpenAI by Abdul RafayAI Agent and RAG Chatbot Development | LangChain and OpenAI by Abdul Rafay
AI Agent and RAG Chatbot Development | LangChain and OpenAIAbdul Rafay
AI agents and chatbots that answer from your data
I build AI agents and RAG chatbots that answer questions from your documents, support your customers and take repeat tasks off your team. Examples include Enterprise Support AI Hub, Panda Chat and ScholarAI.
What you get
RAG chatbot that answers from your PDFs, docs or website
Support agent that answers customers and hands off to a human
Workflow agents that draft, sort and update data for your team
Built with LangChain, LangGraph, OpenAI, Claude or Gemini
Vector search with Pinecone, Supabase or PostgreSQL
Admin panel to manage documents and review chats
Chat widget or full chat page inside your app## How it works
Call and a written plan with your data sources and use cases
Working chatbot on a staging link for you to test
Launch, handover and tuning after launchI track AI cost per user from day one so the feature stays affordable as you grow.## AI agents and chatbots that answer from your data
I build AI agents and RAG chatbots that answer questions from your documents, support your customers and take repeat tasks off your team. Examples include Enterprise Support AI Hub, Panda Chat and ScholarAI.
What you get
RAG chatbot that answers from your PDFs, docs or website
Support agent that answers customers and hands off to a human
Workflow agents that draft, sort and update data for your team
Built with LangChain, LangGraph, OpenAI, Claude or Gemini
Vector search with Pinecone, Supabase or PostgreSQL
Admin panel to manage documents and review chats
Chat widget or full chat page inside your app
How it works
Call and a written plan with your data sources and use cases
Working chatbot on a staging link for you to test
Launch, handover and tuning after launch
I track AI cost per user from day one so the feature stays affordable as you grow.
I build AI agents and RAG chatbots that answer questions from your documents, support your customers and take repeat tasks off your team. Examples include Enterprise Support AI Hub, Panda Chat and ScholarAI.
What you get
RAG chatbot that answers from your PDFs, docs or website
Support agent that answers customers and hands off to a human
Workflow agents that draft, sort and update data for your team
Built with LangChain, LangGraph, OpenAI, Claude or Gemini
Vector search with Pinecone, Supabase or PostgreSQL
Admin panel to manage documents and review chats
Chat widget or full chat page inside your app## How it works
Call and a written plan with your data sources and use cases
Working chatbot on a staging link for you to test
Launch, handover and tuning after launchI track AI cost per user from day one so the feature stays affordable as you grow.## AI agents and chatbots that answer from your data
I build AI agents and RAG chatbots that answer questions from your documents, support your customers and take repeat tasks off your team. Examples include Enterprise Support AI Hub, Panda Chat and ScholarAI.
What you get
RAG chatbot that answers from your PDFs, docs or website
Support agent that answers customers and hands off to a human
Workflow agents that draft, sort and update data for your team
Built with LangChain, LangGraph, OpenAI, Claude or Gemini
Vector search with Pinecone, Supabase or PostgreSQL
Admin panel to manage documents and review chats
Chat widget or full chat page inside your app
How it works
Call and a written plan with your data sources and use cases
Working chatbot on a staging link for you to test
Launch, handover and tuning after launch
I track AI cost per user from day one so the feature stays affordable as you grow.