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About Koidra

AI in the physical world

The AI wave that rewrote the digital economy has barely touched the factories, plants and greenhouses that consume most of its energy. We build for that half.

Book a demo What autonomous operations actually takes

The problem

The data these systems run on is in no text corpus

An operator running a dryer, a boiler or a glasshouse is not working with documents. They make a control decision, watch how the physical system responds, and adjust the next one. That loop produces the only data describing how their particular plant behaves, and it exists nowhere else. It cannot be absorbed by reading manuals or papers.

This is why a language model, however capable, is not a controller for a live process. It reasons over text rather than over state estimated from sensor streams, it does not model thermodynamics or machinery, and it carries none of the guarantees that writing a setpoint back to a plant requires.

Most of what gets called physical AI is about robots: machines that move through the world with cameras and motors. That is a real problem and it is a different one.

The physical world that consumes the most energy is the one already standing still:

  • heating and cooling systems;
  • drying and pasteurization lines;
  • greenhouse climate;
  • boilers and thermal processing.

They run every hour of every day, they account for a large share of the energy the world uses, and almost all of them are still driven by a person or by rules configured decades ago.

Where the only data about a plant comes fromAn operator makes a control decision, the physical system responds, they watch and adjust. That loop produces the only data describing how this plant behaves, and it exists in no document.The operator decidesa setpoint, a valve, a screenThe plant respondsin minutes, or hoursThey watch, and adjustThe only record of how this plant behavesin no manual, paper or corpus
The loop an operator runs every day is the only place this data is made. No amount of reading produces it.

Founder

From Microsoft Research to industrial AI

Koidra’s technology traces its roots to pioneering research at Microsoft Research, where our founder, Dr. Kenneth Tran, led work on reinforcement learning for physical systems: AI that learns from interaction with complex physical environments and can be trusted to act on them. The project was model-based reinforcement learning for operational optimal control: a class of algorithms that are sample efficient, off policy and transferable. That matters because a plant will not give you a million episodes to learn from, and a wrong action costs hardware or a crop.

Under his leadership the Microsoft team won the first international Autonomous Greenhouse Challenge in 2018: the first time a crop was grown remotely on AI, and the only AI entry to finish ahead of the expert growers. A Koidra team won it again in 2022. The method behind both, physics-informed deep learning for simulating and controlling physical systems, is now covered by US patent 12,197,176, granted in January 2025.

Koidra is that research carried into live plants. The same kind of agent that held a competition greenhouse now runs setpoints in commercial greenhouses and in industrial dryers and boilers.

The control framework from the Microsoft Research project. An expert policy seeds a learnable policy by imitation; a probabilistic dynamics model, learned from time series data, drives continual model-based policy optimization; the agent acts on the environment and logs new data back.
Where it started: the AI framework developed at Microsoft Research that laid the foundation for Koidra's autonomous control technology.

See Koidra on your own plant data

Bring a week of readings from one line. We will show you what the platform sees, what it would have flagged, and what it would have scheduled.

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