Case Study · AI & Knowledge Systems
AI-Powered Personalized GPT
A private GPT that actually knows your organization.
Role
AI Engineer & Architect
Category
AI & Knowledge Systems
Technology
- Python
- LangChain
- Pinecone
- Claude AI
- Google Drive API
Overview
Generic chatbots fail the moment someone asks about their organization's own documents, policies, or member records. This project built a personalized GPT system that grounds every response in the organization's actual knowledge: files living in Google Drive and structured member data.
A synchronization pipeline watches Google Drive, extracts and chunks document content, embeds it, and keeps a Pinecone vector index current. At query time, LangChain orchestrates retrieval: the user's question is matched against the vector index and their own member context, and Claude generates a response grounded in the retrieved material rather than in generic model knowledge.
01
The Challenge
The knowledge base was alive — documents changed in Drive daily, and member data varied per user. The system had to stay in sync without manual re-indexing, respect which user could see which data, and answer from retrieved facts instead of hallucinating.
02
The Approach
I built the pipeline in Python with LangChain handling chunking, embedding, and retrieval orchestration. Incremental Drive sync re-indexes only changed files. Retrieval filters combine semantic similarity with per-user access scoping, so responses are both relevant and permission-safe. Prompts constrain Claude to the retrieved context, with source references in responses.
03
The Outcome
Members ask natural-language questions and get accurate, context-aware answers drawn from their organization's real documents and their own data — a knowledge assistant that stays current as the Drive changes.