HCL Foundation · Public health learning
Public Health Graph RAG WhatsApp Assistant
Course knowledge becomes a conversational Graph RAG assistant on WhatsApp.
OutcomeGraph-backed public-health learning answers over WhatsApp
15 days · 2-person team
The Challenge
Public-health course content needed to be queryable through a conversational channel. Simple search was not enough because topics, concepts, and dependencies required relationship-aware retrieval.
The Results
- Graph RAGRelationship-aware knowledge retrieval
- WhatsAppAnswers delivered where users already work
- Graph-backed public-health learning answers over WhatsApp
- Graph RAG, Relationship-aware knowledge retrieval
- WhatsApp, Answers delivered where users already work
- 15 days, POC delivered by a 2-person team
What We Built
We partnered with HCL Foundation · Public health learning to ship a production-ready AI platform for Public Health · E-Learning. We built a content ingestion pipeline that structures learning content into a Neo4j knowledge graph. We combined graph retrieval with Pinecone vector search and OpenAI RAG for richer answering. We kept the engagement as a focused POC/build that proves Graph RAG delivery for public-health learning content.
How We Delivered
Built a content ingestion pipeline that structures learning content into a Neo4j knowledge graph.
Combined graph retrieval with Pinecone vector search and OpenAI RAG for richer answering.
Exposed the assistant through WhatsApp using Twilio for a two-way conversational flow.
Kept the engagement as a focused POC/build that proves Graph RAG delivery for public-health learning content.
Scope of Work
- Graph-RAG knowledge layer for public-health content
- WhatsApp delivery channel for field queries
- Grounded answers from curated health knowledge
- Operator-safe retrieval and response design
Tech Stack
Tools we deployed in production, not just POC'd.
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