From prototype to production: the enterprise AI gap
A prototype proves that a workflow can be made intelligent. Production proves that it can keep working under load, policy, audit, exception handling, and changing institutional reality.
Reliability under real workflows
Security as product behavior
Deployment paths that match the institution
The gap between prototype and production is rarely about whether the demo worked. It is about whether the system can survive the conditions of the institution.
Production AI has to handle partial data, changing users, permission exceptions, audit trails, uptime expectations, and operational pressure. These are product requirements, not implementation details.
Davion treats deployment shape as part of the product. The system has to match the institution, whether that means private cloud, on-premise infrastructure, or a more isolated operating environment.