Intelligence needs infrastructure.
The systems behind the capabilities.
Beyond the model
An intelligent application is only as useful as the systems that let it operate. Data must be available and reliable. Deployment must be practical. Outputs must fit into a workflow that someone actually uses.
Look for the bottleneck
An infrastructure thesis starts with friction: where do teams spend time, where does complexity accumulate, and what prevents a promising product from reaching a customer? Those questions help distinguish a useful enabling layer from another tool in an already crowded stack.
Our perspective
We are interested in both applied intelligence and the foundations beneath it. The connection is practical: stronger infrastructure makes new applications possible, and useful applications expose the next infrastructure need.