Your teams get AI colleagues that amplify their work. Your security team gets something rarer: agents that can never see more than the person asking — enforced by deterministic controls that sit outside the model, on infrastructure that is yours alone.
Agents always act as the user — never as a shared super-account.
Access rules enforced outside the AI. Same result, every time.
Personal and regulated data masked before storage or display.
Dedicated agents, compute, and storage. Region of your choice.
Every request flows through four layers. The AI operates in the middle two — but the decisions about what it may see are made above and below it, by controls it cannot bypass.
Access starts with who you are — not what you ask.
When someone joins a project channel in Slack, a secure auth persona is created for them and access is assigned based on their role on that project.
Each person gets a single governed identity that carries their exact permissions into every request. Agents always act as the user — never as a shared super-account.
Notion, Jira, and other connected tools authorize with each person's own account. Tools only ever see what that person can see.
Agents answer questions, draft work, and keep projects moving. They request information on the user's behalf — but they never decide what data they're allowed to see. That decision is made below them, by controls they cannot bypass.
These run outside the AI. They are deterministic rules, so they apply the same way every time — even if an agent tries to reach beyond what a user is allowed to access. The agent physically cannot see what these layers remove.
Company memory is tagged with the channel each piece of information came from. Before any result reaches an agent, this layer checks the asking user's channel access and removes everything they aren't a member of.
A separate pass scans content for personal and regulated data — names, emails, phone numbers, IDs, and other PII — and masks or anonymizes it before it is stored or shown. This holds even when the underlying source contains sensitive details.
Every customer is fully separated. Agents, compute, and storage are hosted independently — down to the region. There is no shared data path between customers, so one customer's information can never reach another's.
Each new customer gets the same isolated environment.
The system deploys into your world — not the other way around.
Agents run inference on your enterprise LLM API — no customer data leaves your existing model environment or your existing agreements with your model provider.
Deployment is available in any customer-nominated cloud region, keeping data residency aligned with your regulatory and sovereignty requirements.
Usage and inference limits are set by you, so spend and consumption stay predictable and within the boundaries your organization approves.
Enterprise procurement shouldn't take six weeks of email ping-pong. Everything your legal, privacy, and security teams need is packaged and ready.
A complete MSA with dedicated schedules for data processing (DPA), data sovereignty, and service levels — structured for review by your counsel from day one.
PII detection, masking, and anonymization are built into the data path itself — not bolted on. Personal data is protected before it is ever stored or displayed.
Permissions mirror your existing channel and tool access. No new access model to govern, no standing super-user credentials to audit.
Start with a scoped, fixed-fee pilot across one or two squads. Prove value inside your own environment before any broader commitment.