Where industrial AI
The problem that hasn't been solved
Most industrial AI projects produce dashboards, not decisions. The failure mode isn't the model. The gap is between inference and action. Data arrives late, insights go unread, and operators make the same calls they always made. Closing that gap is harder than building the model, and it's where most deployments stall.
Why edge changes the equation
For a class of industrial problems, cloud-first is the wrong architecture. The signal has to travel too far, the latency budget is too tight, and the data often can't leave the site. Edge inference closes it because it matches the physics of the problem, not because it's newer. Some decisions have to happen on the floor, in milliseconds, without a round trip.
What agentic systems change
The shift from "AI flags something, human decides" to "AI reasons across signals and takes bounded action" is the transition from monitoring to operations. In manufacturing, the bottleneck is operator attention, not sensor coverage. An agent that can close a loop without a human in the path changes what's possible. It doesn't remove the operator; it reserves their attention for decisions that actually need it.
Where we think this goes
In the next two to three years, the meaningful divide won't be between companies that use AI and those that don't. It will be between companies whose systems act and companies whose systems report. The infrastructure for this (real-time edge pipelines, agentic loops, multimodal perception) exists today. The work is integration and deployment, not research.
What we're building toward
The projects in our Showcase are pieces of this answer, each one deployed in an industrial setting. The problems are real, the constraints are real, and the systems are running. See the work →