Quinn for enterprise

An AI workforce your security team can actually approve.

The same Quinn your teams use in chat, with the control a large organization requires. Per-tenant isolation, no standing credentials, signed and replayable actions, and a fleet-wide kill switch. This is how an AI workforce touches production at all.

The control model

A few humans stay on the wheel, by design.

Irreversible actions stop

Anything that touches production surfaces the diff in the channel and waits for a human tap. Nothing irreversible runs unattended.

No standing credentials

The runner holds none. Access is brokered just-in-time, scoped to the action, and revoked after.

Signed and replayable

Every action is cryptographically signed, recorded, and reconstructable end to end, so any action can be audited or replayed.

Blast-radius cap and kill switch

Per-tenant isolation under a published data-boundary contract, a cap on how far one action can reach, and a fleet-wide stop we demonstrate live.

Scale across the org

Every team's work makes the next team faster.

Each team self-serves its own tools and automations, scoped to the people who own them. What one team builds becomes a starting point the next team can install, and a fix learned in one place shows up ready in another. The organization compounds instead of rebuilding the same thing in ten silos.

How it adds up
  • Per-team isolation, central visibility
  • Shared capabilities, installed not rebuilt
  • Fixes that carry across teams
  • One bill, usage you can see by team
What a security review requires

Built as a workstream, not a slide.

01

Per-tenant isolation

A published data-boundary contract that says, in writing, what crosses and what never does.

02

Attestation and audit

The artifacts a real security review asks for, ready before the review starts, with a full audit trail.

03

Indemnity path

The contractual backstop that lets a CISO sign off on an AI workforce touching production.

No lock-in by construction

The reason procurement can say yes.

We never train a frontier model

We are the neutral execution layer, not a model lab. The two roles cannot sit in the same vendor.

We never go single-model

The model is swappable underneath. You are never locked to one lab's roadmap or pricing.

We never read across the tenant wall

Your data stays yours. Quinn learns from outcomes, not from reading anyone's systems.

Full export from day one. The execution rail ships as an open, forkable standard, so you are never captive to a single vendor.

Bring an AI workforce into your org, on your terms.

Start in Discord today, or contact us to scope a security review and rollout.