Scaling AI Context Across Dev and Strategy Teams with Isolated Memory, Open Standards, and Migration Readiness

Most discussions about AI memory start from a narrow use case: one person, one chat interface, one context bucket. That is not how real companies work. Even a small company quickly ends up with different types of context that should not all live together: engineering decisions strategy notes operational procedures founder-only context project-specific memory future AI worker or automation context That was the real problem I wanted to solve. Not just: how do I make an AI assistant remember more? But: how do I make company context reusable across tools and teams, while keeping boundaries clear, staying interoperable, and avoiding lock-in? For me, this had to work first in a developer workflow — especially VS Code and Claude Code — but it also had to make sense for strategy, operations, and future internal AI workflows. So I stopped thinking about memory as a convenience feature. I started treating it as infrastructure. The real problem is not memory. It is boundaries. AI memory is useful