The story behind
Recall Racoon
A deterministic cognitive control plane for AI memory. Built around a simple idea: saved knowledge should be governed, searchable, and useful when the work returns.
Once upon a
raccoon...
The Raccoon's Problem
There was once a raccoon who could remember that he had carefully hidden his marbles. The problem was remembering where he put them. Every day became filled with raccoon responsibilities—trash to rummage through, interesting objects to investigate, nighttime adventures, and the perpetual need to keep his famous dark eye mask looking just right. Through all the hustle and bustle, he would remember that the marbles had been stored safely somewhere. He simply could not recall where.
The Lesson
Saving something is not the same as being able to recall it when it matters. The raccoon had a Stash (the hiding spot) but lacked Rumi (the intelligence to organize and retrieve). He had Rummage (the searching) but no Recall (the finding).
The Idea
That became the idea behind Recall Racoon. Businesses and AI systems create information constantly. The problem is not simply storing it. The problem is understanding it, organizing it, connecting it, governing it, and making it useful later. Rumi handles the hustle. Rummage processes the incoming stream. The Stash remembers. Recall retrieves.
Rumi works in the background so your people and AI systems do not have to become memory librarians.
Rumi does not replace your AI tools. Rumi helps them remember.
Rumi. Rummage.
Stash. Recall.
Rumi
The intelligence layer
Work with incoming information and stored knowledge through processing and conversation.
Rummage
What Rumi does
Ingest, enrich, classify, and organize information for the Stash.
The Stash
The memory layer
Persistent, searchable, governed memory with claim lifecycles and source context.
Recall
Retrieval when needed
Bring relevant knowledge back into the workflow, within its intended scope.
How we build
and why
Deterministic memory policy
Keep memory rules explicit and separate from model reasoning.
Local processing
Run configured models in your environment. Define integrations and data flows deliberately.
Governance at write time
Apply admission rules, preserve provenance, and route claims through their lifecycle.
Your Stash, in view
Inspect, update, and export stored knowledge through the available interfaces.
Connected workflows
Use MCP and REST to connect memory to compatible agents and applications.
Context that continues
Keep useful memory available when a project resumes.