FusedFrames watches your team's on-screen actions in real time and builds a queryable knowledge base from actual behavior. When the AI can't infer why a user made a particular decision — an unusual workflow branch, a non-obvious choice — it pauses and asks the user to explain their reasoning in plain language. That explanation is merged with the recorded action into a structured pattern: trigger, steps, reasoning, and outcome. Over time, this becomes a library of institutional knowledge grounded in real work, not documentation that goes stale. AI agents can then query those patterns over an API or CLI, giving them the context that normally lives only in experienced team members' heads. Aimed at macOS teams that want their AI tools to act on accumulated expertise rather than generic instructions.