Every company's know-how is scattered across chats, Slack, docs and people's heads. Recall compiles it into versioned, executable playbooks — new hires ramp on them in days, and your AI agents run on the same brain.
A company works because humans vaguely remember how things get done. New hires take months to learn it — and AI agents can't operate on “vaguely remembered” at all.
How you really handle setup, reviews, or an incident lives in Slack scrollback and senior engineers' memory — not anywhere usable.
Wikis go stale the day they're written, and a chatbot over documents just returns text — it can't reliably do the work.
You can't safely point an AI agent at real work without an executable, trusted procedure for how your company does it.
Recall pulls signal from AI chats, Slack, docs and a short expert interview, and compiles a structured, executable playbook — steps, guardrails, and your team's own fixes.
A department lead reviews the draft and approves the version. Only signed playbooks ever run — the trust step that makes automation safe.
A new hire walks it step-by-step; an AI agent loads the same playbook via MCP. Every run is audited.
The same compiled playbook serves a human ramping up and an AI agent doing the work. Onboarding proves each playbook is correct; once it's trusted, your agents run on it too.
The output is a runnable procedure with steps and guardrails — not a search result you still have to interpret.
Every playbook is human-approved and versioned. Proven on new hires before an agent ever executes it unattended.
Ships as an MCP server, so any AI assistant — Claude, ChatGPT, Copilot — runs on your brain, audited end to end.
When someone gets stuck, the fix that unblocked them becomes the next version. The brain improves as the company runs.
Recall never reads your chats in the background, and nothing becomes a playbook until a person approves it — so the brain fills with signal, and your team stays in control of what runs.
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