your AI work, carried forward.

Real AI work no longer happens in one place. You plan in Claude, research in ChatGPT, edit in Cursor, and ship with Codex while source truth stays in the systems you chose. Every tool that already holds context has a business reason to keep it there. Linear cannot say works equally well outside Linear. Notion cannot say your context follows you to Cursor and Claude Code. Microsoft Copilot is bound to M365 by license. ChatGPT and Claude memory are single-provider by design. Cursor memories live in one IDE. Glean is bound to the enterprise search motion.

3ngram is the layer with no incentive to keep you inside one walled garden. Cross-surface is the wedge, and it is the moat: a typed work-state that travels between tools because no incumbent can credibly serve it without conceding their own ground. That is what makes shared context defensible instead of feature-parity.

A decision made in one chat, a blocker explained to a coding agent, a client commitment buried in a transcript, and the document that proves it all may describe the same work. Tomorrow you brief another AI from scratch, or you carry the continuity yourself. 3ngram gives the AI tools you already use a shared work state: decisions with rationale, commitments with lifecycle status, blockers with resolution state, and explicit origin metadata. Retrieval is how you understand it first. Follow-through begins with briefings and explicit queries that bring open work back into the next session.