Ambience: Institutional Context Layer - Institutional context layer for agents

Ambience captures every AI agent session your team runs (Claude Code, Cursor, custom agents), redacts it, scopes it across personal / team/project/org, and makes it available for any other agent in the organisation. The next session starts smarter. We have built a conflict graph that extracts reason for decisions being made and flags conflicting information to keep the whole team aligned.

We’ve seen the institutional memory problem firsthand while selling and deploying agents into some of the world’s most forward-thinking companies - including Mistral, Intercom, Gong, and n8n. Our team is one of the most AI-native teams in the world processing, over a trillion tokens per month, and still no shared memory between agents. Every agent session started from zero. Decisions, customer context, workflow preferences, failures, and hard-won lessons were trapped in people’s heads, scattered in .md files or lost inside past sessions. Learning and company context doesn’t compound. That is painful in a 50-person company with one year of context and decisions made by agents. It becomes existential in a 5,000-person company deploying thousands - eventually millions - of agents. Our belief is simple: over the next 6 to 18 months, agents will move into every business function - and non-technical teams will discover that their institutional memory was never built for machines.

Ambience: Institutional Context Layer - Institutional context layer for agents

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