Human-in-the-loop
AI creates candidates, but responsible planners review, adjust, and approve before a schedule is published.
AI compliance
AI that can be understood, reviewed, and controlled
Caire builds AI as decision support for planning, not autonomous public authority decisions. Recommendations should be transparent, planner-approved, and reviewable after the fact.
Current page
AI compliance
Human-in-the-loop, AI governance, and review trails.
Responsible AI
AI controls that belong in the product
Caire Core collects recommendations, user decisions, and outcomes in a continuous learning loop where improvements are reviewed by responsible users before they affect production.
Transparency
Users should understand why a recommendation appears, which goals it affects, and what trade-offs it creates.
Reviewability
Recommendations, changes, and decisions are saved as operational traces for follow-up and internal control.
Risk-aware scope
AI is used for planning and operational support with clear boundaries, responsibilities, and escalation paths.
EU AI Act
Caire treats AI scheduling as operational decision support. The EU AI Act is considered through transparency, documented scope, human oversight, and risk-aware controls rather than unverified certification claims.
Transparent and explainable
Recommendations are tied to configured goals and constraints such as travel time, continuity, skills, and time windows. Users can review why a candidate schedule was generated before publishing.
Human approval
Human planners remain responsible for final review. They can override recommendations, compare alternatives, and approve the published schedule.
Data privacy and sovereignty
Customer operating data is handled for the organization’s workflow. Caire does not position public scheduling optimization as hidden generative-model training on customer records.
Caire Core
The same architecture that optimizes schedules also records the goals, constraints, and user choices around each recommendation.
Documentation-ready
The product is designed to help organizations explain data sources, human oversight, evaluation routines, and operational controls when they document their AI use.