Every AI remembers now. Ours knows who's allowed to
Yohanun is the memory layer for AI that handles things that matter. Your AI remembers everything it should, reveals nothing it shouldn't, and can prove both — to you, your clients, and your auditors.
The difference isn't memory. It's what governs it.
A vector database with a chat wrapper is table stakes. Yohanun gives your AI what a trusted colleague has: a memory of what matters, discretion about who hears what, and the habit of getting better at the job every week.
Memory that behaves like memory
Not a pile of embeddings. Recency fades, importance persists, sessions connect — temporal decay and salience are computed at recall, the way memory actually works.
- • Vector + graph hybrid storage
- • Temporal decay & salience ranking
- • Cross-session, cross-thread recall
Confidences that keep themselves
Ask about something you're not entitled to and the answer simply doesn't contain it. No clever prompt changes that — the decision happens before the model is ever involved, and every read leaves a trail your compliance team can walk.
- • Walls between matters, clients, and roles
- • Revoke access — gone on the next request
- • Provable to an auditor, read by read
Experience that compounds
Report an outcome — success, failure, correction — and it becomes a retrievable lesson. Memory that helped ranks higher next time; memory that hurt ranks lower.
- • Outcome feedback loop
- • Per-memory track record
- • Ranking that adapts to results
Everyone secures your documents. Nobody governs what your AI remembers.
Your document permissions don't follow your data into the AI. The moment it's embedded, remembered, or learned from, it becomes a new kind of data your ACLs have never heard of — and the only thing standing between it and the wrong user is a system prompt that says "please don't reveal confidential data."
That's not access control. That's a request. One clever prompt and your matter files, HR records, or client data walk out the front door.
If you're in a regulated industry, "we asked the model nicely" is not a compliance story.
The model can't leak what it never saw.
With Yohanun, memory the asker isn't entitled to never reaches the model. Not filtered out of the answer — never a candidate in the first place.
That's why no prompt injection works here: the access decision already happened, in the database, before the conversation could touch it.
Someone changes teams, leaves a matter, exits the firm? Revoke once and it's done on their next question. Every read is audited. That's a compliance story.
Four questions. Answered by the platform, not the prompt.
Every serious AI deployment eventually hits the same four questions. Yohanun answers all of them with outcomes you can demonstrate — not instructions you hope the model follows.
Who can see it?
Only the people cleared for a matter ever see its memory — with real ethical walls that block conflicted access before it's even granted. And when someone leaves a matter, their access ends before their next question. No reindexing, no redeployment, no window.
Who owns it?
Private context stays private, and shared knowledge is deliberate — never an accident of being in the same database. Retire an agent or hand a matter to a successor in one step: nothing copied, nothing lost, nothing exposed, history fully intact.
What may it do?
Agents you can actually leave alone: free to act inside explicit limits, while anything beyond the line reaches a human before it happens, never after. Silent overreach isn't unlikely — it isn't possible.
What did it learn?
Every success, failure, and correction makes the next answer better — advice that worked rises, advice that hurt sinks. The accumulated experience is an asset your organization owns, and it survives every model swap.
Swap the model any time — OpenAI, Anthropic, Google, DeepSeek, or local. The memory, the governance, and the accumulated experience live below the model. That's the moat.
Built for AI that handles things that matter
When the data is privileged, the actions have consequences, and the auditors are real — that's where governed memory stops being a feature and becomes the product.
Regulated-Industry Copilots
Law, finance, healthcare: matter walls, conflict checks, and per-read audit trails — enforced by the platform, provable to a regulator.
Agent Fleets with Boundaries
Teams of agents with private memory, shared house knowledge, and explicit mandates — each one knows what it may see and how far it may act.
Grounded Domain Experts
Experts that answer from a governed corpus — graded by authority, grounded in sources, cited every time — plus a private layer that remembers each user.
Give your AI a memory you can defend
Persistent memory, deterministic governance, and a learning loop — below the model, under your control. Tell us what you're building and we'll show you how it fits.