Your AI tools don't have a memory problem. They have an architecture problem.

Every new session starts from zero. Projects blur together, an old document gets treated as current, and the assistant confidently continues from stale state — so you spend your time re-teaching history instead of doing the work.

I build the continuity layer that fixes that: a knowledge architecture that lets your AI assistants keep your projects, rules, and source-of-truth straight across tools and models (ChatGPT, Claude, and others), with explicit rules for which source wins when they disagree — and hard stops when the authoritative source can't be established, instead of guessing.

This is the same architecture behind my VERA Context Bridge / Global Continuity Router case study, productized as a scoped setup for your stack.

How it works:

Continuity audit — we map your projects, tools, documents, and trackers, and find where stale state, project bleed, and re-teaching are actually costing you.

Architecture design — project activation, authority ordering, and source-of-truth locations for each project, designed around how you already work.

Build — the continuity package itself: routing/activation rules, project isolation, recovery structure, and onboarding context packages.

Test & handoff — we run real sessions against the system, break it on purpose, fix the failure points, and I walk you through running it without me.

Best for: founders, consultants, and small teams juggling multiple AI tools or long-running projects, who are tired of being their AI's memory.

Not a fit if: you use one AI tool for one-off questions, or you want a chatbot built — this is the architecture layer underneath the tools, not an app.

Attribution: I own problem framing, system/workflow design, constraints, evaluation standards, failure diagnosis, and documentation. AI platforms provide implementation assistance under that direction.