Ipnops provides compute and interfaces for physical and digital AI.
Ipnops provides compute and interfaces for robotics, machines, infrastructure and software, physical and digital. It integrates with Living Worlds for simulation, training and lives in digital worlds. Each row says what Ipnops does and gives one example.
Where an AI is put to work.
Robotics, machines and infrastructure are physical. Software is digital. The same interface serves all of them.
Humanoids, mobile robots, arms and quadrupeds trained in simulated worlds and reached through their twins. Example: a navigation policy trained among simulated pedestrians, exported as ONNX and run in the twin before it runs on the robot.
Industrial machines, vehicles and drones read over standard buses, observe-only, with a person approving anything that moves. Example: signed drive readings feed a twin and a forecast; a person approves each setpoint.
Buildings, plants, grids and water: meters and controllers read into twins. Example: a meter's readings enter a building twin, with a forecast and an alert when a reading leaves its band.
Simulated people living lives over decades under a stated seed, with what-if branches and a scorer for births. Example: ten runs of the same seed produce the same life courses.
Agents and programs run in isolated environments and reach other software through a declared interface. Example: a coding agent runs your tests and shows its work in a terminal or browser.
Where it is in use.
Each row is a workflow that runs today.
Studios, venues, audio engineering, music production and post: room response, standard acoustic parameters, loudness and speech tools. Example: capture a sweep, get the room's impulse response and parameters, compare them with a reference and sign the report.
Room acoustics before a room is built, and temperature, comfort and daylight for a described space. Example: describe a room's walls, openings and weather, and read temperature, comfort and daylight over a year.
Simulation, motion, perception and policy training for robots and vehicles. Example: train in simulation, plan a motion and write the trajectory for review.
Crowd and pedestrian simulation for places people move through. Example: test how a robot crosses a plaza among simulated crowds.
Forecasts from station and gridded data, with intervals. Example: forecast the next day of a sensor's series and compare it with the repeat of the last value.
Industrial buses and edge devices read into twins. Example: a meter on a register bus feeds a signed series and a forecast.
Product and garment 3D: a measured body, a pattern and a cloth drape, rendered before anything is sewn. Example: fit a garment on a digital body and read the result from a render.
Tell us your industry.
Name the job and the data you have. We scope the run and the output with you.