The AI Operator
One AI employee you talk to, that stands up and runs every other agent in your business.
The pitch everywhere is "deploy your first agent in fifteen minutes." The problem was never the first agent. It's the fortieth — and who runs them, who notices when one breaks, and who retires the one nobody needed.
And the seven things we have it run — listed in the order we'd actually do them, not alphabetically.
AI Estate Audit
Find out what you've actually built before anyone asks you to spend money.
The problem: Most owners a year into this can't tell you what they've actually built, what it costs, or which critical job is really one person's undocumented script.
Local AI Deployment & Cost Architecture
Move the repetitive work off per-token billing and onto hardware you own.
The problem: Every AI pilot dies on the invoice, not on the technology. The bill grows every single time the automation succeeds, which is a strange thing to punish.
Guardian Layer — Safe AI Outbound
The gate that goes in before AI is allowed to talk to a customer.
The problem: The fear was never that it wouldn't work. It's that one day it says something you would never say, to a customer, in your name.
Fleet Intelligence
A ranked morning list for the shop, generated overnight for the price of electricity.
The problem: Your shop finds out a truck is broken when it's already out on a route with a crew on it.
Territory Intelligence
Every system you own, joined on geography — plus the public data nobody bothers to pull.
The problem: You think you know your territory. Then you put it on a map and the data disagrees with you.
Decision Review Panel
Put the plan in front of independent reviewers who can't see each other's answers.
The problem: The expensive mistakes are never the ones somebody flagged. They're the ones everyone in the room agreed on.
Field Vision & Operational Robotics Dataset
in developmentMeasurement today. The training data robotics will need tomorrow.
The problem: The race for physical-world training data is already on — the big labs are paying for it. What most operators do not realize is that they are already generating it: skilled physical work, in real cluttered rooms, photographed every day. It just walks out the door unstructured and unowned.
What has to be true for this to work
We take on work we can make succeed. These are the conditions we look for on a first call — if one is missing, we'll usually tell you what to do about it before we quote anything.
- Enough volume to justify owning hardware. The savings come from repetition. Below a certain throughput the honest answer is a cloud API, and we'd rather say that on day one than a year in.
- Data you trust enough to act on. Where your systems disagree, we fix or reconcile that first. It's usually the fastest win in the whole engagement.
- Someone who can say what "right" looks like. One person on your side with the authority to make the call. That relationship is what turns a working demo into a working business.
- Agreement that a human signs off on the way out. Anything that reaches a customer, an employee or a bank account waits for a person. We build it that way on purpose.
Start with the audit.
It's small, it's fixed price, and it tells you what you've actually got before anybody asks you to spend real money.
The AI Estate Audit