Every reply feels like it remembers them.
Your assistant recalls each customer's history, preferences, and past issues — so conversations pick up where they left off, and answers stay grounded in what's actually true.
- user
- agent
- tool / web
- ×2
- ×1
- ×2
- ×1
Two ways a bot loses trust.
Your bot greets every returning customer like a stranger — or worse, confidently invents an order that never existed. Either way, trust drops.
Forgets the customer
Your bot greets every returning customer like a stranger, so each conversation starts from zero.
Makes things up
Or worse — it confidently invents an order that never existed. Either way, trust drops.
Pick up every conversation mid-thread.
One customer's facts — name, plan, past tickets, preferences — kept as a tidy lane, each one showing where it came from. Returning customers feel known.
- user
- agent
- tool / web
- ×2
- ×1
- ×2
- ×1
It only says what it can stand behind.
Every remembered fact is tied to a source — a real ticket, a verified purchase, or what the user told you. When facts contradict, they resolve to the current truth. If it isn't grounded, the bot doesn't assert it.
Tied to a source
Every remembered fact links back to a real ticket, a verified purchase, or what the user told you.
Stays current
When something changes, the new truth supersedes the old — so the bot answers from what's true now.
No invented facts
If it isn't grounded, the bot doesn't assert it. It only says what it can stand behind.
One customer's memory never bleeds into another's.
Each end-user's memory is walled off, so nothing leaks between customers. You control retention and deletion — concrete, regulator-friendly, and enterprise-ready.
Isolated per user
One customer's memory never bleeds into another's — each end-user's memory is walled off.
You control retention
Set how long each customer's memory lives, and change it whenever you need to.
Delete on request
Remove a customer's memory on demand — built for the privacy promises you make.
Memory that adds no perceptible lag.
Recall in sub-200ms p95 so memory keeps up with a live conversation, and it feeds the model a clean, relevant slice so answers stay sharp at scale. Works with any model.
A maintained target so memory keeps pace with live chat — no perceptible lag.
These are our own targets and eval numbers, clearly labelled as such until independent benchmarks land.
Make your assistant feel personal — safely.
Personal, continuous support — without the bot inventing details or mixing up users.