Research and perspectives on the architecture, infrastructure, and engineering required to make AI Deployment work inside complex organizations.
AI Infrastructure
Deploying an LLM inside an enterprise is not a model-selection problem. It is an integration problem at the boundary between probabilistic inference and deterministic systems.
AI Deployment
A working AI prototype proves that a model can perform a task. Deployment requires proving that the same capability can operate repeatedly, under real data, real permissions, real workloads, and real failure conditions.
AI Infrastructure
Once AI enters an operational workflow, the organization needs more than a model and an integration. It needs explicit contracts for what the system can access, what it can execute, how it fails, and how its behavior is changed over time.