For teams where alignment beats speed.

The problem isn't missing documentation — it's disappearing context

Mature teams often have plenty of documentation. The challenge, as one product owner put it in our discovery, is that the context behind a decision gradually disappears. People remember what was implemented but not the assumptions, trade-offs, and constraints that led there. When someone asks “does this still hold?”, the reasoning has decayed and the debate reopens.

For architecture and platform work, that decay is expensive. A single re-litigated decision can stall a quarter, and onboarding a new engineer means reconstructing history through conversations with long-tenured colleagues — which does not scale.

Brief the agent, keep the human as the expert

Decision Memory hands the agent the approved decisions with their rationale, the alternatives the team rejected and why, and the constraints and document rules that apply. The agent drafts or reviews a technical document already knowing the direction — so the reviewer isn't correcting context, they're evaluating substance.

Maintaining continuity between the initial intent and the final outcome is the hard part. Decision Memory preserves it.

The agent returns alignment findings and any conflicts it found against existing decisions, the assumptions it made, the open questions it couldn't close, and proposed updates to memory. A human decides what becomes trusted.

A decision layer across your existing tools

This does not replace Jira, Confluence, or a tool like Jira Product Discovery. Those track execution and preserve pages; Decision Memory preserves decision-level history and makes it retrievable even when you don't know the exact keyword or where it was first discussed. It's the decision layer across the tools you already use.