A premade pipeline and a forward-deployed engineer on-site, whether you build next-gen sensors or run industrial operations.
It already drives the Waymos in San Francisco. Tomorrow it will be everywhere, from underground mines to satellites.
Nvidia's Jensen Huang calls it a $40 trillion opportunity, and the companies that adopt first will own it. Just like coding agents made software cheap and fast to build, the right infrastructure makes physical AI cheap and fast to adopt.
Industrial companies hire consultants who rebuild the same AI infrastructure from scratch on every project. The wheel keeps getting reinvented.
Open-ended consulting spend that scales with hours, not outcomes.
Way too many months to even the first pilot deployment, without end in sight.
Your core capability lives inside someone else's company.
Every new use case means another contract, another team.
You start with a ready-made pipeline and an engineer on-site, and you choose who builds the AI on top, in-house or outsourced.
The sensor-to-deployment pipeline is already built. No reinventing the wheel of AI infrastructure, just lower cost and a smoother rollout.
An engineer on-site, integrating in one to two weeks. Plug and play instead of a custom consultant solution that never quite ships.
Build the AI in-house, outsource it, or mix both. You own the pipeline and the IP — and decide who develops the model on top.
The hardware you build, or already run on the floor.
Ingest, training and deployment. The part every company rebuilds from scratch.
Running in production, in the cloud or on edge hardware like NVIDIA Jetson, and owned by you.
You skip rebuilding the same plumbing every time, keep full ownership of your data and models, and put your effort into the problem you actually want to solve.
with an expensive problem a model could solve
who need the pipeline behind them