Stop hoping people will follow the rules. Start enforcing them from one dashboard.
The assistants will change — Claude today, Gemini next quarter, something that doesn't exist yet after that. Your company's AI playbook shouldn't have to change with them.
configuration | noun
Company-wide instruction baked into every AI assistant, ensuring consistency without relying on individual behavior.
How it works
Define once. Push everywhere.
Company-wide AI playbook
Define your tone, data handling policies, approved workflows, and guardrails in one place, then push them to every assistant via MCP (Model Context Protocol — the open standard that lets AI tools receive configuration from external systems). A new hire on day one gets the same AI setup as someone who's been using it for a year.
Department-level configurations
What engineering needs from AI is fundamentally different from what sales or legal needs. Configure each function independently and enforce the rules automatically across every assistant.
Vendor-independent by design
Built on MCP, the open standard for AI tool integration. When you switch assistants, add new ones, or sunset old ones, your configurations come with you — no migration, no rework.
How a company works is too important to be locked into one AI vendor.
Manage configurations, skills, and tools from a single dashboard. Every department gets the right setup. Every assistant gets the right rules.
The problem
Three pillars of the AI governance gap
- Invisible
No department-level data on who uses AI or how
- Ungoverned
No shared playbook — 200 people, 200 setups
- Unaccountable
No ROI proof — just anecdotes and gut feel