About

The story behind
Recall Racoon

A deterministic cognitive control plane for LLM systems. Built by engineers who hit the same walls you're hitting.

Origin Story

Once upon a
raccoon...

01

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.

02

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).

03

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.

Core Concepts

Rumi. Rummage.
Stash. Recall.

Rumi

The intelligence layer

Examines incoming information, identifies what matters, prepares it for the Stash.

Rummage

What Rumi does

Understand, summarize, categorize, connect—incoming info becomes organized memory.

The Stash

The memory layer

Persistent, searchable, governed. Claims with lifecycle, citations, provenance.

Recall

Retrieval when needed

Hybrid search, graph context, freshness scoring. Sub-50ms latency.

Our Values

How we build
and why

Deterministic over probabilistic

Rules, not guesses. Same input = same decision. Auditability is architecture.

Local-first, cloud-optional

Run fully offline. Add cloud when you need scale, not because you're forced to.

Governance at write time

Validate at ingest. Bad memory never enters the Stash. Probes: format, citation, semantic, contradiction.

Your data, your rules

We never see your Stash. Architecture enforces this, not policy. No training on your data.

MCP-native, not wrapper

Built on the protocol, not around it. 13 tools. stdio, SSE, HTTP. Zero config.

Capacity-based, predictable

No per-token surprises. No unlimited promises. Transparent limits and overage rates.

Join Us

Building the memory
layer for AI