Predictable latency
Indexed retrieval is designed to fetch data directly, avoiding the variable cost of scanning approximate matches.
Panaxon AI · M.N.E.M.O.
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.
Shared memory
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
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.
Indexed retrieval is designed to fetch data directly, avoiding the variable cost of scanning approximate matches.
Structured, hierarchical indexing is built to hold up as memory volume grows, not just at small scale.
Deterministic lookups are intended to reduce the drift that comes from approximate similarity matching.
See it live
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
If you're building agents, copilots, or memory-heavy context pipelines, Panaxon AI can discuss fit, architecture, and access.