Automate Client Research Reports with RAG

Overview

Consultants spend hours researching clients, industries, and competitors before engagements. This workflow builds a RAG (Retrieval-Augmented Generation) system that can ingest client documents, industry reports, and public data — then answer research questions with sourced, accurate responses.

Steps

1. Set up the document pipeline: Build a Python script that ingests documents (PDFs, presentations, reports) and chunks them for embedding using LangChain's document loaders.
2. Create the vector store: Embed document chunks using OpenAI embeddings and store them in Pinecone for fast similarity search.
3. Build the RAG chain: Configure a LangChain retrieval chain that fetches relevant document chunks and feeds them to the LLM as context for answering questions.
4. Add the Streamlit interface: Build a chat interface where consultants can ask questions about the client and get sourced answers with citations to specific documents.
5. Deploy for the team: Package and deploy the app so your consulting team can use it for client preparation, competitive analysis, and research synthesis.

What you'll learn

• How RAG works and why it reduces hallucination in enterprise contexts
• Building production-ready document ingestion pipelines
• Deploying internal AI tools for consulting teams