Memory that makes your AI sharper, not slower.
Most “memory” dumps a pile of old chat history into your AI every turn — and a flooded AI gets slow, vague, and wrong. ULTRAMEMORY hands the model just the few facts that matter, so it stays fast and on-point. Even with ten agents writing to one memory, each recall stays a handful of clean facts.
More memory, better answers — not a bloated, confused model.
Stuff in too much history and your AI gets duller.
AI models have a limited attention span. Stuff too much history into one request and quality drops — the model starts missing the point, contradicting itself, and slowing down. The industry calls it context rot. You've felt it: the longer a chat gets, the dumber it seems.
A wall of old chat dumped into every turn.
The few facts that actually matter, this turn.
- Ships to the EU only
- Prefers email
- Plan = Team
- Primary contact: Dana
- Renews in March
We send the least that matters.
We don't paste your history back in. We pull out the durable facts, throw away the noise, keep only what's true right now, and send the model a short, clean brief — every time.
Extract the facts
We read what happened and pull out the durable facts — then throw away the noise.
Keep only what's current
We keep what's true right now and let superseded facts fall away, so nothing stale gets injected.
Send a short brief
The model gets a short, clean brief of just the facts that matter — every time.
See what a clean brief looks like.
A fixed context budget fills with a few solid facts we keep — while the rest falls away, each with a reason. We even log what we left out, and why.
- 212 raw chat messagesno durable fact
- Plan = Freesuperseded
- Address restated 6×duplicate
- Old region = USno longer true
Quality held while a raw-dump baseline degrades.
As memory grows, a raw-history baseline floods the prompt and quality slips. Because we inject compact, current facts, quality stays high — and recall stays fast.
- Answer quality, held as memory grows
- Context injected per recall (lower is better)
- Distractors in the prompt (lower is better)
Illustrative; exact figures come from our eval harness.
Shared by ten agents, still clean.
When many agents share one memory, the temptation to dump everything is even bigger — and the rot is worse. Because we inject compact facts, a shared memory used by ten agents stays as clean as one used by one.
More memory, sharper answers.
A short, clean brief to the model — quality held, recall fast. That's both must-wins at once. Free to start, pay for what you use.