I’ve been spending a lot of my nights and weekends on Jennah. It kind of nagged its way into existence: AI agents are getting genuinely good, but the memory underneath them is still held together with tape. Everyone builds it the same way: a vector database for recall, a separate graph database for reasoning, an execution log off in the corner, and a pile of app-side glue trying to keep the three in sync. That’s the thing I wanted to address. Jennah’s whole idea is one store; execution logs, vector recall, and reasoning graphs living together in a single store per agent, instead of a vector DB bolted onto a graph DB bolted onto a log.

And because it’s all in one place, I can commit a whole reasoning step atomically, then query across all of an agent’s memory in a single request - semantic recall, graph reasoning, and recent history fused into one ranked, consistent answer, instead of me stitching three systems together and hoping they agree. And I wanted memory data to be consistent regardless of whether it’s single region, multi-region (within a geography), or globally distributed. That’s the part I keep geeking out over. The hard problem isn’t storing any one kind of memory, it’s making them behave as one. It’s exactly the kind of thing I love as a CTO — unglamorous, foundational, the sort of problem that, if designed right, nobody ever has to think about again. Which is a weird thing to be excited about at 1am, but here we are.

More to come soon.

(If you’re an investor in the space, you can contact me anytime.)