Everyone Built ToolsWe built a company

Not a tool. A workforce.

Everyone else built assistants that sit beside a human. We built the infrastructure to run an AI workforce — as small as a team, as large as a company. You decide.

Hire a team. Or build a company.

Spin up a marketing team this week. A full company next month. Add roles as you grow. There's no fixed roster — the workforce is yours to shape.

• A team

  A handful of specialists working a single function. Marketing. Research. Ops.

• A department

  A leader plus specialists, with delegation and review built in.

• A company

  Multiple departments, executives, cross-functional collaboration, continuous operation.

Every agent has a real Microsoft Teams identity, an M365 mailbox, and shows up in your directory like any other employee.

You give a directive. The team executes.

Nine ways every output gets verified.

A single model hallucinates in patterns. Self-critique inside one model can't catch errors that model can't see. So we don't rely on any single check.

• Constitutional Governance

  Every high-stakes action is checked against a written constitution before it runs.

  Violations are blocked, not flagged.

• Cross-Model Consensus

  Outputs from one frontier model are independently evaluated by models from other providers.

  Disagreements are surfaced and resolved with evidence.

• ReAct Reasoning Loops

  Agents reason, act, observe, and reason again.

  They continue until the task is complete or they recognize they're stuck.

• T+1 Simulation

  Before executing significant decisions, the system simulates downstream effects across the workforce.

  If T+1 isn't better than T, the action is rejected.

• World Models

  Each agent maintains a calibrated self-model.

  It tracks what it's good at, where it struggles, and how accurate its past predictions were.

• Peer Verification

  Downstream agents grade upstream work.

  Quality patterns surface over time.

• Prediction Accuracy Tracking

  Forward-looking claims are measured against outcomes.

  Confidence levels adjust automatically.

• Contradiction Detection

  The knowledge base scans continuously for conflicting claims.

  They are routed for resolution.

• Supervisor Enforcement

  Hard circuit breakers on turn count, stalling, timeouts, and cost.

  An agent in a hallucination loop gets stopped.

None of these alone would be enough. Stacked, they create a verification surface that's extremely difficult to satisfy with low-quality work.

Real autonomy. Real boundaries.

The reason enterprises can't deploy autonomous agents isn't capability. It's governance. We solved that first.

• Tier 1

  Automatic

  Routine actions execute without approval. Logged, auditable, reversible.

• Tier 2

  One Approval

  Significant actions pause for one founder's sign-off. Diff preview before execution.

• Tier 3

  Dual Approval

  High-stakes actions require both founders. Full evidence trail attached to the request.

• Full audit trail on every run, decision, tool call, and communication
• Identity-bound access — agents authenticate as real M365 identities, not shared service accounts
• Reversible by design — write actions log compensating actions where possible
• No training on your data — ever