Controlled environment

Test data, architecture, models and AI systems before they are trusted.

Examine how data is structured and connected, measure its quality, test models and automated workflows, and apply governance before outputs are relied upon or released.

• Data architectureTrace sources, structures, movement and dependencies
• Data qualityTest completeness, freshness and reliability
• Models and agentsTest bounded behaviour and outputs
• Governance and costApply controls, record decisions and monitor use

Enterprise relevance

Enterprise controls, made observable at a manageable scale.

The sandbox does not reproduce the size or operational complexity of an enterprise platform. It applies enterprise-oriented practices to small working implementations so the controls can be examined and tested.

Operational hereConnected data, running models, agent workflows, access roles, quality checks, logs and review decisions.

Simulated hereOrganizational roles, approval stages, policy scenarios and selected failure conditions.

Outside this sandboxEnterprise scale, formal certification, production service levels, procurement and security operations.

What it demonstratesWhether architecture and governance controls work inside a functioning system, not only in documentation.

Platform connections

Bring connected platforms into one controlled testing environment.

Connected now

Toronto Public Transit Service Pulse

Transit experiments, journey evidence and operational data controls.

Planned connection

Community Atlas

Community Atlas uses machine learning and geospatial evidence to support community planning in emerging countries, including places where local data is incomplete or changing quickly. Not connected yet.

Public architecture explainerSee how the three Journey Agents are structuredExplore the concepts, evidence labels and safeguards without entering the private workspace.