Built for AI that handles things that matter
When the data is privileged, the actions have consequences, and the auditors are real, a vector database with a chat wrapper isn't enough. These are the use cases where governed memory stops being a feature and becomes the product.
Firm AI with real ethical walls
A law firm's AI has to know things its users individually may not see. Matter walls, conflict-of-interest screens, and classification levels aren't features you can prompt into a model — they have to be enforced before retrieval, every time.
Yohanun labels every memory with its matter and classification, checks clearances against a platform-owned ledger, and blocks conflicted grants at the graph level. When a lawyer leaves a matter, one revocation takes effect on the next request. When a matter changes hands, custody transfers with a single ledger entry — no data migration, no provenance rewritten.
The Wall in Action
Agent fleets with boundaries
Deploy a team of AI agents where each has private memory, all share house knowledge, and none can act beyond its mandate. Each agent's memory is owner-walled in the same store; shared knowledge is a custody grant, not a copy.
Mandates give each agent an explicit action limit — commit freely below the line, escalate to a human above it. Retire an agent and transfer its memory custody to a successor with one ledger entry.
This isn't hypothetical: we run our own multi-project engineering agents on exactly this architecture, every day.
A Mandate, Not a Prompt
# Agent asks to act
POST /api/access/authorize-action
{ "action": "issue_refund", "amount": 180 }
# Under its €250 mandate → allowed, audited
{ "decision": "allow" }
# Over the line → escalates to a human queue
{ "action": "issue_refund", "amount": 4800 }
{ "decision": "escalate",
"escalation_id": "esc_91…" }
# No mandate, no unit match → never silently allowed.
# Commits fail closed.Grounded experts on a governed corpus
Some domains — law, theology, medicine, standards — have authoritative sources with different weights. An expert system there can't just retrieve "similar text." It has to answer from the corpus, carry each source's authority grade into the answer, cite everything, and refuse to freelance beyond what the sources support.
Yohanun powers this as a two-layer architecture: a shared, curated corpus everyone reads, plus a private per-user layer — their documents, their history, their threads — walled by ownership in the same gate. The expert knows the field and remembers you.
Two Layers, One Gate
The same primitives, other domains
Labeled memory, cleared principals, mandates, audit, and outcome learning compose into whatever your domain calls its version of "trust."
Healthcare
Patient context that only returns to cleared roles, with a per-read audit trail — enforcement your compliance team can verify on every single read, not attest to once a year.
Financial Services
Chinese walls between desks, spending mandates on agent actions, and an immutable record of who saw what and who approved what.
Enterprise Copilots
Company-wide assistants where HR memory doesn't surface in engineering chats — departmental walls enforced in retrieval, not requested in prompts.
Customer Operations
Agents that remember every customer across channels, act within refund/discount mandates, and escalate the rest to humans — with the audit trail built in.
Personal Assistants
Assistants that build understanding over months — memory that decays like memory, owned by the user, portable across whatever model runs it.
DevOps & Incident Response
Systems that remember past incidents, learn which fixes actually worked via outcome feedback, and know exactly which actions need a human sign-off.
What does trust look like in your domain?
Tell us your use case and constraints — we'll map them onto the platform's primitives and show you exactly what's enforced where.