Every machine needs to know where it is.
Humanoids, robots and drones see the world differently. We give each one a current picture of it.
Human spaces were never designed for robots.
Humanoids are walking into warehouses, factories, hospitals and public buildings built around people: narrow aisles, doors, stairs, shelves at arm height, and people everywhere. To be useful, they need to understand those spaces the way we do.
What they need
- Precise indoor maps, down to shelves, doors and handles
- Knowing what objects are and where they belong
- Awareness of people and how spaces are used over the day
What Kaiinos delivers
- Semantic digital twins of buildings, room by room
- Object and shelf-level labels for picking and manipulation
- Occupancy and flow patterns learned from sensor history
- Simulation environments to train and test tasks before go-live
- Tote 14 located · rack B3, shelf 2
- Grasp point sent to robot
- Route clear of 2 people
Illustrative example
Robots that know the place they work.
Industrial robots, cobots, inspection quadrupeds and field robots already work in real facilities. What slows them down is a world that changes faster than their maps, and not knowing which asset is which.
What they need
- Maps that stay current as layouts and inventory move
- Knowing exactly which valve, gauge or machine they are looking at
- Safe routes around people and moving equipment
What Kaiinos delivers
- Change detection with map updates pushed to the fleet
- Inspection missions linked to a geospatial asset register
- Dynamic keep-out and safety zones
- Maintenance signals built from every inspection
- Gauge 7 read · 4.2 bar
- Linked to asset PR-07
- Next checkpoint · valve 12
Illustrative example
Machines that cover ground nobody mapped this morning.
Drones, autonomous vehicles, AMRs, UGVs and vessels move on their own, from warehouse floors to open terrain, where layouts, infrastructure, weather and water keep changing. They need a world model that is current, not just accurate.
What they need
- Up-to-date terrain and infrastructure over large areas
- Weather, water and seasonal change
- Making safe decisions when the link drops
What Kaiinos delivers
- Base maps fused from satellite, drone and sensor data
- Mission planning over terrain and asset models
- Anomaly detection and change tracking across every flight
- Compressed edge models for onboard decisions
- Hotspot · panel 4-07
- Linked to asset P-4-07
- Maintenance ticket drafted
Illustrative example
The same foundation. Tuned for each machine.
| What machines get | Humanoids | Robotics | Autonomous systems |
|---|---|---|---|
| 3D spatial mapGeometry of the space, indoors or out | |||
| Semantic labelsWhat every object and surface is | |||
| Digital twinA living model that updates as the place changes | |||
| Simulation environmentTest missions and edge cases before go-live | |||
| Live change updatesMap changes pushed to machines in operation | |||
| People and traffic awarenessWhere people and vehicles move, and when | |||
| Terrain and weatherSlopes, ground, water and conditions | |||
| Edge modelsIntelligence that runs on the machine itself |
From first scan to first deployment.
Start with one site and one machine. Prove it there, then scale.
Pick a site and a machine
We agree on one environment, one robot and the mission that matters most.
Capture and model
We capture the site and build its digital environment on the Kaiinos platform.
Simulate the missions
Routes, tasks and edge cases are tested before the robot arrives.
Deploy and keep it current
The robot goes live on a living digital twin that updates as the environment changes.
Bring us the machine. We'll bring the world it works in.
Tell us what you're building and where it needs to operate.
Start a pilothello@kaiinos.ai