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Koidra
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The agentic system for industrial processes

Physics-aware AI agents watch your process and equipment around the clock, find the root causes for an incident or loss of yield, and recommend the actions. The actions can be further executed by Koidra.

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Proof

AI that understands your operations and runs them like an expert

Aerial view of a commercial glasshouse complex, several ranges of glass with the packhouse and buffer tanks alongside
2×
Winner, Autonomous Greenhouse Challenge 2018 and 2022, a live crop each time
19.5%
Yield increase compared with the same crop grown by hand next to it
17%
Energy savings in commercial greenhouse operations
Up to 20%
Gain in process efficiency through autonomous AI control

The solutions

Two complementary AI-powered solutions

  1. Process Optimization All your operational data in one place, an AI process engineer available 24/7, and autonomous process control with safety guardrails built in. Essential Universal data integration: all your operational data in one place, live and historical Pro The AI Consultant: an AI process engineer, available 24/7 KoPilot Autonomous process control: proven recommendations, applied every minute
  2. Intelligent Asset & Maintenance Management Work orders, preventive maintenance, inspection routes and spare parts, on every technician's phone. Essential Modern maintenance management (a CMMS), set up by your own team Pro Predictive maintenance, from evidence to work order

Compare the two solutions and their plans.

Industries

Koidra’s solutions apply across a wide range of manufacturing industries, and to greenhouse farming, which we treat as a crop production factory. All of them run a physical process around the clock, with machines to maintain, setpoints to adjust, standardized operating procedures to follow, and energy as a large share of the cost.

Koidra models each of them the same way, as a knowledge graph of how the plant is built and what drives its performance. On top of it sit the physics models Koidra knows best: energy models, greenhouse climate models, and thermodynamic processes such as drying, heating and combustion.

The knowledge graph under the digital twinAreas and lines hold the assets, each with its specs and sub-assets, the assets upstream that feed it, the parts fitted to it and its work orders. Metrics hold all the numeric data over time: an asset's operating conditions, from sensors, setpoints, operator entries and formulas, and the performance of each production batch, such as yield, energy per tonne and quality. The whole graph is the backbone of Koidra's physics-informed digital twin, which grounds the AI agent's root causes and recommendations.groundsKnowledge graphholdfeedfitted toonoperating conditionsperformance of eachAreas and linesAssetsspecs, sub-assetsUpstream assetsPartsWork ordersMetricsall numeric data over timeProduction batchyield, energy per tonne,qualityDigital twinphysics-informed modelsThe AI agentroot causes, recommendations
An asset's specs and the assets that feed it set its operating conditions. Metrics record those conditions and each batch's performance over time, and the digital twin is built on that chain.

What our customers say

Koidra's system has changed how we grow. I no longer watch the conditions all day and keep adjusting the climate settings: KoPilot does it, fully automatically, and brings our crop measurements into every climate decision. And whenever we need help, the Koidra team responds right away.
Victor Vazquez-Gomez COO, High Tech Group, Mexico
Koidra’s dashboard visualizes the plant’s condition and real-time performance, enabling me to monitor it remotely. Thanks to up-to-date insights, I can lead cross-function teams, improve processes in a timely manner, and make data-driven business decisions.
Trang Do CI Manager, Ayo Biomass, Vietnam

All your operational data in one place

Koidra keeps sensor readings, energy meters, equipment history, maintenance work and the readings operators type on their phones together, tied to the same machine and the same production batch. Most plants already have this data, but in separate systems. AI can only find the cause of a problem in data it can see, so it needs all of it in one place.

Physics-aware AI, so every recommendation fits your plant

Our models are grounded in how heat, mass and machinery actually behave, rather than fitted to last quarter’s numbers. The model knows a dryer heats only as fast as its mass allows, so the AI never recommends a change the dryer cannot make.

This physics-informed method is where our research heritage sits.

Answers grounded in physics and dataThe plant's own data and the physics of its process feed Koidra's physics-informed machine-learning models. The AI agent works on top of those models, and what reaches the team is analysis, recommendations and a conversation about the plant.Operational dataKoidra's physics-informed modelsProcess physicsThe AI agentAnalysisRecommendationsConversations
Your data and the physics of your process meet in one model, and the AI agent works on top of it.

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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