benned for business
Your robots should know your business. Not the average of everyone else's.
Robot AI today is trained on synthetic data — the statistical average of every site and every task. Kin captures what is learned in the field, at your sites, on your jobs, and transfers it to every machine you run.
Last updated: July 2026
The problem
Three ways robot knowledge fails a business today
01
Synthetic training
Robots arrive trained on simulations and generic demonstrations. They know everything in general and nothing about you. Your floors, your sites, your standards — all missing on day one.
02
Knowledge lock-in
What a robot learns on the job stays in that robot. When it breaks, gets replaced, or reaches end of lease, months of site experience are discarded with it.
03
Vendor lock-in
When learning does persist, it usually lives on the manufacturer's platform. The manufacturer owns it. Switch vendors and you start from zero — with knowledge your own operation paid to produce.
How Kin changes it
One Kin per site or fleet. Every machine inherits it.
A Kin entity holds all field-learned knowledge for a site or a fleet. Machines connect to it and inherit what came before. A new robot has full context on day one — the layout, the exceptions, the standards, the things somebody had to learn the hard way.
Replace a machine: nothing is lost. Replace a vendor: the knowledge stays yours, because it never lived inside the vendor's hardware in the first place. One robot's months of site experience becomes every robot's starting point.
Kin is the same personal-AI architecture we build for people, applied to a business. Read more about the underlying shift in physical AI.
The detail is the moat
Not "clean the floor". Which floor, which chemistry, which corner.
Synthetic training
A cleaning robot trained on synthetic data knows what a floor is.
Your Kin
Your Kin knows the third-floor corridor is natural marble, the lobby is composite that scratches, and the skirting behind the radiators only fits a side brush at 40 degrees.
That level of detail is what clients judge you on — and it is exactly the level generic training data cannot contain, because it is different at every site. It has to be learned in the field. Kin is where it goes.
Sectors
Where field-learned knowledge pays first
The economics
The hardware is becoming a commodity. The knowledge is not.
Roughly 16,000 humanoid robots were deployed globally in 2025. Goldman Sachs projects a $38 billion humanoid market by 2035, and Bank of America expects unit prices to fall toward $17,000 by 2030. Robots-as-a-service is already priced around $10–12 per hour against $30 per hour for human labor.
When every competitor can lease the same machine at the same rate, the machine stops being the differentiator. What differentiates is what the machine knows when it starts your shift.
Businesses that own their field knowledge compound it: every job makes every future job better. Businesses that rent their knowledge from a vendor start from zero with every new machine — and hand their operational edge to whoever owns the platform.
Questions
Common questions
What is field-learned knowledge?
Everything a robot — or the people working around it — learns on the job that was not in the factory training. Which surface needs which treatment. Where the ground is soft. What this client counts as done. Kin captures it at full detail and keeps it, independent of any single machine.
Does this replace the robot manufacturer's AI?
No. The manufacturer’s model still handles motion, balance, and general skills. Kin layers your context on top: the site-specific and company-specific knowledge that generic training cannot contain.
Who owns the knowledge?
You do. Kin is your entity. What your fleet learns at your sites belongs to your business — not to a robot vendor, and not to us. Replace a machine or a vendor and the knowledge stays.
Which robots does it work with?
Kin is designed to be machine-independent and builds on the standards the industry already runs on: transport over ROS 2, DDS or MQTT, skills in VLA and LeRobot-compatible form, spatial context aligned with OpenUSD. It holds knowledge at the level of the site and the task, so it can brief a humanoid, a wheeled platform, or a heavy machine. Talk to us about the specific hardware in your fleet.
Your fleet is learning things right now. Where does it go?
If it goes nowhere — or to your vendor — that is the problem Kin exists to fix.