The memory my AI runs on — and the reason I never start from zero.
Every AI session starts blank. Close the tab and it forgets your systems, your decisions, your context — so the next session begins with re-explaining everything. Fine for a chatbot. Useless for running a business.
And it isn’t just an AI problem. Most businesses run the same way: the context lives in people’s heads — and when the person leaves, or just goes home, the memory goes with them.
Sinew is a persistent memory-and-tools layer I built for my own AI — running on Azure SQL, connected through the Model Context Protocol. Every working session ends with a logged record of what was decided, built, or corrected. The next session — on the same machine or a different one entirely — picks up exactly where the last one left off.
In daily use since early 2026. Months of decisions, builds, and corrections — and nothing explained twice.
When I say I build AI into how a business runs, this is the level I mean — not clever prompts, but infrastructure: memory, tools, continuity. Every system I build for a client gets the same discipline: context captured, decisions logged, nothing lost between sessions or people.
If your business depends on knowledge that lives in one person’s head — the pricing rules, the customer history, the way the old system actually works — that’s the same problem. And it’s solvable.
Thirty minutes. We’ll look at where your business’s memory actually lives — and what it would take to make it survive.
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