From trained once to learning in deployment.
A model’s weights capture what was known when it was trained. An agent fixing a codebase, answering a customer or picking up a part learns things no training set contained: this repository’s conventions, this customer’s history, this gripper’s quirks at this site.
That experience is valuable precisely because it is specific. It is also dangerous for the same reason. It goes stale, it can be wrong, and it can be planted. A machine that remembers more without knowing what deserves to be remembered just repeats its mistakes for longer.
Memory becomes the system of record.
Every application that keeps state needs a database. Every machine that learns from what it does will need a memory, and that memory will hold the most consequential data an organization has about its AI: what its agents and robots believe, where it came from, who approved it, and what happened when it was used.
That is why memory cannot be a cache. It needs time, provenance, scope, review, erasure and an audit trail. UniMem was built that way from the first record.
Memory must prove that it helps.
Retrieval answers whether a memory is relevant. It cannot answer whether using it made the outcome better. That is a causal question, and the only honest way to answer it is to compare outcomes with and without the memory on comparable tasks.
UniMem runs that comparison continuously, for every memory, on live work, and refuses to report a result from a broken experiment. Over time, evidence about which kinds of memory help in which contexts becomes the asset: it tells a new deployment what to trust before it has learned anything itself.
One memory for every intelligent machine.
The same memory model and evidence engine already extends beyond memories: to skills, to optimizer edits, to the writers that create memories, and to robot fleets sharing what each robot learns. The aim is a single, governed layer between experience and action for any machine that learns from what it does.
We are building in the open and raising for the next stage. The product and its limitations are available to inspect. Our ambition is broad; our next steps are concrete: validate real workflows, earn adoption, and grow from evidence.
Build the memory layer with us.
Try the local loop, or bring one workflow to a pilot conversation.
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