← PortfolioCase Study · SaaS Product (Founder-led)

BotWhisperer: RAG-Enabled SaaS Chatbot Platform

A production SaaS where businesses create custom RAG chatbots without writing code.

Next.jsLangChainPineconeOpenAISaaSRAG
BotWhisperer: RAG-Enabled SaaS Chatbot Platform
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The Challenge

Businesses wanted ChatGPT-style assistants trained on their own data - product docs, FAQs, policies - without hiring an AI team or managing vector databases themselves.

The product needed multi-tenant isolation, simple onboarding, reliable retrieval, and a polished chat interface that non-technical users could deploy in minutes.

Technical Approach

I architected and built the full stack - document ingestion pipeline, embedding and indexing with Pinecone, LangChain retrieval chains, and a Next.js frontend with streaming responses.

  1. Designed multi-tenant data model with per-bot vector namespaces in Pinecone
  2. Built document upload pipeline supporting PDF, text, and URL ingestion
  3. Implemented chunking strategy with metadata for source attribution in answers
  4. Created LangChain retrieval chain with reranking for answer precision
  5. Shipped streaming chat UI with citation links back to source documents
  6. Deployed on Vercel + managed Pinecone with monitoring and error tracking

Tech Stack

Next.jsTypeScriptLangChainPineconeOpenAI APIPostgresVercelStripe

Outcomes

Full-stack ownership

Designed, built, deployed, and currently maintain the entire platform.

Production RAG

Live multi-tenant RAG pipeline serving real business customers.

Self-serve onboarding

Non-technical users can create and deploy a custom chatbot without engineering help.

FAQ

Project FAQ

The core RAG pipeline MVP shipped in approximately 3 weeks on average. Production hardening - multi-tenancy, billing, monitoring, and UI polish - took an additional 10 weeks.

Need a similar AI product built?

I build RAG systems, AI agents, and full-stack AI products for startups.