I will build an ai rag chatbot for your business knowledge


About this gig
I build AI chatbots that answer using YOUR data not generic guesses. Powered by RAG (Retrieval-Augmented Generation) with vector search over your docs, policies, or knowledge base.
What I can build for you:
- Knowledge base ingestion from your existing docs, FAQs, or internal wikis
- Semantic search-grounded AI replies the bot only answers from what it actually knows
- Multi-agent delegation a supervisor agent that hands off to specialist sub-agents (email, CRM, lookups)
- Automatic escalation to a human when the AI isn't confident, instead of guessing
- Integration with Gmail, Slack, or your existing support inbox
I've built and tested this exact architecture a hierarchical multi-agent system using Supabase pgvector for retrieval and specialist sub-agents for actions like sending emails or updating CRM records.
Message me what knowledge base you want it grounded in and I'll scope the right package for you.
Get to know Muhtadi
n8n Automation Engineer, AI Agents and Workflow Automation Specialist
- FromPakistan
- Member sinceDec 2025
- Avg. response time1 hour
Languages
Urdu, English
My Portfolio
FAQ
What counts as "my data"?
Any text-based content — FAQs, SLAs, pricing sheets, internal wikis, product docs.
How is this different from just using ChatGPT ?
ChatGPT doesn't know your business. This bot is grounded in your actual documents via vector search, so answers are specific and accurate to you.
Will it ever make things up?
No — if the answer isn't in your data, it escalates to a human instead of guessing. Built into every package
Can it also take actions, not just answer questions?
Yes, at the Multi-Agent System tier — it can delegate to sub-agents that update your CRM or send emails.
Do I need a vector database already set up?
No — I set it up as part of the build (Supabase pgvector or Pinecone).

