Ardua AI
Use AI with sensitive information while retaining control over model access and permitted actions. Ardua AI applies your organization's rules to governed AI requests, supports local and approved cloud models, and records the decisions behind each execution.
On governed execution paths, restrictions are checked before information reaches a model. Those restrictions carry forward when new data enters an analysis, so earlier cloud permission cannot override later data-handling requirements.
Use local models for private processing and approved cloud models for eligible work. Ardua AI separates application behavior from model access. If no suitable model is permitted, the request is refused rather than sent somewhere unauthorized.
For tools and data access executed through the platform, Ardua AI checks permission before acting on a model's proposal. A model cannot give itself additional authority. Your team defines the task and its permitted boundaries.
Records identify the rules applied, provider used, and reasons for allowing or blocking an action. Investigate data handling without routinely duplicating sensitive source data in the governance log.
Public-sector and data-sovereign deployment
For governments and institutions with strict data-residency requirements or constrained recurring budgets, Ardua AI supports an operating model built around locally controlled infrastructure.
Discuss a data-sovereign deploymentRoute restricted workloads to approved local models before information leaves the institution.
Apply capital investment to infrastructure that can serve many workloads instead of incurring a fee for every model interaction.
Retain access to approved external models for eligible work when their capabilities justify the cost and data handling.
Keep core AI capabilities available where connectivity, external services, or payment channels are constrained.
Private and controlled-hybrid deployment