I took an archived graph database, added vector search, LLM embeddings, and a full GDS suite – now I'm aiming to make it the central engine for healthcare AI. Here’s the architecture, the roadmap, and why I believe this is the right foundation for clinical analytics.

A month ago, I found Kùzu – an embedded analytical graph database that had been archived. I was curious, so I forked it and started poking around. I didn’t build this from scratch; I’m standing on the shoulders of the original authors, who did the heavy lifting of the core engine. My contributions so far are modest: a few C++ API extensions, some parser tweaks, and a handful of extensions that I thought might be useful.

This is very much a learning project. I’m not a database expert – I’m a software engineer who enjoys building systems and is fascinated by the intersection of graphs, AI, and healthcare. This post is my attempt to document what I’ve understood so far, where I see potential, and what I’m still struggling with.

2. What I've Actually Done (Honestly)

Let me be upfront: I did not write a database engine. The Kùzu team spent years building the columnar storage, the OpenCypher query processor, the ACID transaction manager, and the vectorized execution engine. That's PhD-level work, and I respect it deeply.