1 · Chatbot
An AI chatbot for your private data
Ask questions in plain language and get back the exact values you stored — with role-based access, so sensitive data stays scoped to the right people.
Panaxon AI · M.N.E.M.O.
AI memory that never guesses.
Vector-free retrieval — M.N.E.M.O., short for Multidimensional Neural Elastic Memory Organization, returns the exact fact you stored, not the closest match by similarity. Built on OAT, a patent-pending deterministic index, so every query resolves to what's actually there.
One platform, three-in-one
One product, three ways to put your private data to work.
M.N.E.M.O. isn't three separate products. It's one memory platform with three-in-one functionality: the same exact-recall engine answers your questions, connects you to knowledge other people publish, and plugs into the RAG pipeline you already run. Panaxon AI isn't just selling AI memory as a service — it's AI memory as a platform.
2 · Knowledge network
A network to publish and subscribe
Tenants can publish private data and subscribe to what others publish, then query all of it from one ongoing relationship instead of a pile of links.
3 · Memory platform
MnemoRAG inside your own pipeline
Call M.N.E.M.O. from your RAG pipeline with one stateless request. The retrieval is exact, so the LLM synthesizes from the right context instead of a near match.
Shared memory
One subscription, many publishers — and knowledge people can actually find.
Beyond powering your own agents, M.N.E.M.O. lets a person or company publish what they know and lets others subscribe to it on purpose. Two things make that different from typical sharing: it aggregates unrelated publishers into a single subscription, and it makes what's published genuinely discoverable — not just accessible to whoever has the link.
Vector-free
RAG without vectors — exact recall instead of an approximate guess.
Retrieval-augmented generation just means retrieving context before generating an answer. Vector search — approximate nearest-neighbor lookup over dense embeddings — is the common way to do that retrieval, not a requirement of it. M.N.E.M.O. does RAG through OAT, a patent-pending deterministic index: a query resolves to the exact stored fact, not the closest match by distance. That doesn't eliminate every source of error an LLM can introduce, but it removes the biggest one — being handed the wrong context in the first place, and not knowing it.
Predictable latency
Indexed retrieval is designed to fetch data directly, avoiding the variable cost of scanning approximate matches.
Scale without drift
Structured, hierarchical indexing is built to hold up as memory volume grows, not just at small scale.
Exact recall
Deterministic lookups are intended to reduce the drift that comes from approximate similarity matching.
RAG with MNEMO
Keep your RAG pipeline. Swap the retrieval for exact recall.
RAG with MNEMO means your existing pipeline sends its question to M.N.E.M.O. instead of a vector database. M.N.E.M.O. ingests your data, uses an LLM — Anthropic, OpenAI or Google — only to work out where to look, and retrieves the exact facts through OAT. The answer your LLM synthesizes is grounded in what you actually stored, not the closest match. Your data stays in a Panaxon-hosted database or your own.
Customer RAG pipelineYour app, agent or copilot
MNEMO
Ingest
Inference
AI / LLM
OAT
OAT
AnthropicOpenAIGoogle
Customer dataExact facts, stored as OAT
Panaxon DB
orCustomer DB
Customer-ownedMNEMO coreOAT retrievalLLM providerPanaxon-hosted storage
See it live
Dorothy: a reference app built entirely on M.N.E.M.O.
Dorothy is Panaxon AI's own conversational interface, showing what persistent, structured memory feels like day to day. It's one example of what the API can power, not a limit on what you can build with it.
Next step
Get API access
If you're building agents, copilots, or memory-heavy context pipelines, Panaxon AI can discuss fit, architecture, and access.