How we
work together.
Most AI tools impress in a demo, then stall the moment real work hits them. I build the opposite: systems that get more useful with age, because they're grounded in evidence, shaped around how you actually work, and built to keep paying back long after the pilot.
Everything I do runs on the same engines I build in the open, webdevOS and cofounderOS. The four practices below are how I put that discipline to work for you.
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Memory That Scales
It worked great, then it drifted, and I didn't know how to fix it.
Your setup worked well at first, then it started to drift and you couldn't pin down why. That's almost always memory: the architecture that fit in month one stops fitting as the system grows.
It starts with a discovery call to find where the drift is coming from. Memory should be right-sized to the need and able to grow with it. Whether I deploy it for you or build it with you, I match the architecture to where you really are, from a simple file store like Obsidian up to hybrid retrieval across conventional SQL storage, vector databases, and a knowledge graph. The important part is the path between them, so the simple version is a stepping stone, not a dead end you rip out later.
You walk away with a deployed memory system that's right-sized for today and scales without drifting. Your existing knowledge comes across cleanly, so the system keeps getting more useful with age instead of quietly degrading.
- D.01A deployed memory system, right-sized to where you are now.
- D.02A clear path to scale, from a simple file store like Obsidian up to hybrid retrieval across SQL storage, vector databases, and a knowledge graph.
- D.03Your existing knowledge migrated across cleanly, with no loss on the way.
My setup worked great, then it drifted and I couldn't fix it. What happened?
That's almost always a memory problem. The architecture that was right in the first month stops being right as the system grows, and the symptom is drift: it quietly gets less reliable. The fix is matching the memory architecture to where you actually are now, with a path to grow.
Do I need a complex knowledge graph, or is something simpler fine?
It depends entirely on the need. Sometimes a simple Obsidian setup is exactly right; sometimes you need a knowledge graph with embeddings and a hybrid-retrieval architecture. I right-size it to where you are rather than over-engineering, because the wrong-sized memory is what causes drift in the first place.
If I start simple, will I have to rip it out later to scale up?
No, that's the part I design for deliberately. I map the path from the simple setup to the more complex one, so the early version is a stepping stone rather than a dead end. The whole idea is memory that scales without drifting.
How do you stop the drift from coming back?
By diagnosing what caused it and building the architecture so it stays right-sized as the system grows. The outcome is memory that keeps the system getting more useful with age instead of quietly degrading, and where it helps, I provide ongoing monitoring and support to keep it that way.
Not sure which fits?
The four practices map to the four stages of adoption. Most people start with Find the Value and grow from there.