Centralized model control for enterprise AI
Gain centralized visibility into model performance, usage, cost, and access, so you can choose the right models, manage change responsibility, and scale AI without losing control.
Used by leading security and AI transformation teams
- KOA
- Northwestern
- ArcelorMittal
- BuzzFeed
- Learning Care Group
- Mars
Run models on your terms
From selection through retirement, Airia gives platform teams the controls to evaluate, govern, and adapt models in production without rebuilding integrations or compromising compliance.
Choose models based on performance, not assumptions
Compare models across real tasks, evaluate tradeoffs in accuracy and cost, and route usage based on business priorities instead of vendor preference.
Control model access and availability
Define which models are available to specific teams, projects, or workflows. Enforce structured rollout and prevent uncontrolled experimentation that creates compliance or cost risk.
Adapt to your existing infrastructure
Support cloud, hybrid, or private deployments based on your compliance and data requirements. Evolve your AI strategy without being constrained by vendor architecture.
Stay flexible as the landscape changes
Avoid lock-in to one model. Compare and adjust model availability as new capabilities or pricing structures emerge so your AI strategy remains future-ready.
Confidence that lasts beyond deployment
Track performance, monitor usage patterns, and detect anomalies as models evolve. Maintain stability and accountability even as providers update versions or introduce new capabilities.
Model evolution
Compare versions before rollout and introduce changes deliberately, reducing risk to production workflows.
Performance insights
Track quality, latency, and usage trends so decisions are based on evidence rather than assumptions.
Governance and auditability
Ensure changes are traceable and reviewable so AI systems remain trusted across teams and leadership.
