AI & Data Services for CEA Sector
AI Competencies for Controlled Agriculture
From data pipelines to predictive analytics — proven in real operation
We have been building systems for data collection, interpretation, and use since 2010. Today, we offer these competencies to external partners in the controlled environment agriculture (CEA) sector — as consultations, integrations, or complete data solutions.
Since 2010
Data systems and systems thinking
TEF AI
Digital Europe — AI testing in CEA
VŠB-TUO
FEI, FS, CEET — academic collaboration
What we offer
AI Consultations for Agritech
Analysis of data flows in your operation, identification of AI application points, and solution architecture design. Based on our experience with cloud-managed cultivation.
Data Pipelines and Integration
Design and implementation of data pipelines — from IoT sensors via cloud to analytical dashboards. ETL processes, API integration, real-time data processing.
IoT + AI Sensor Optimization
Configuration of sensor networks (temp, humidity, lighting, nutrients) with an AI layer for predictive maintenance and measurement interval optimization.
Predictive Growth Analytics
Models for yield forecasting, anomaly detection, and growth cycle optimization. Trained on real data from our 3rd generation fogponic systems.
Cloud Platform for CEA
Development and deployment of cloud control systems for controlled environments. Automation of growth cycles, remote monitoring, multi-site management.
AI Reporting — ESG and Yields
Automated generation of ESG reports, carbon footprint tracking, and yield predictions. Transparent data for investors and operational decisions.
Why is simple automation not enough in CEA?
From reactive control to predictive intelligence.
Traditional Automation (Reactive)
The system responds to what has already happened. If the temperature exceeds a limit, it turns on cooling. This often leads to overshooting and plant stress as the system "chases" reality.
AI-Driven Approach (Proactive)
Our models work with data vectors. The AI knows that the sun will rise in 30 minutes and outdoor humidity will drop. It adjusts parameters in advance to keep the plant at the ideal photosynthetic point (VPD) without a single stress spike.
Multi-factorial Analysis
While a human can track 2–3 parameters at once, our AI evaluates the real-time relationships between 15+ variables — from light spectrum to nutrient solution composition and air velocity at the leaf level.
How we cooperate
From first contact to deployment in operation
Audit and Discovery
Mapping your data flows, sensors, and operational processes. Identifying where AI brings measurable value.
Design and Integration
Designing solution architecture, selecting technologies, and connecting to your existing infrastructure. Iterative work with pilot operation.
Deployment and Handover
Deploying solution to production, training your team, and handing over documentation. Offering ongoing support and optimization.
Our AI Platform Architecture
From Edge sensors to Cloud models.
Edge AI (Intelligence in Operation)
Critical decision-making processes (e.g., emergency stop or misting adjustment) run directly on-site. We minimize latency and ensure operation even during connectivity outages.
Data Lake & Time-Series
We use optimized time-series databases that allow us to store and lightning-fast analyze millions of data points from cultivation cycles.
Algorithmic Core
Computer Vision for leaf area analysis, LSTM networks for yield forecasting, and genetic algorithms for finding the climate "golden recipe."
Where does AI change cultivation economics?
Anomaly Detection (Crop Insurance)
The system identified a pump vibration anomaly 12 hours before failure. A crop worth 10,000 EUR was saved by a timely service intervention.
Energy Optimization (Dynamic Lighting)
The model connects solar radiation predictions with spot market prices. The result? An 18% reduction in lighting costs while maintaining the same biomass.
Growth Cycle Shortening
By analyzing data from 50 previous cycles, the AI proposed a change in the temperature curve. The cycle was shortened from 28 to 25 days — one extra harvest per year.
Where we verify it
Our AI competencies are not theoretical — we develop them within real projects and academic collaborations.
TEF AI (Digital Europe)
Testing and Experimentation Facility — testing AI in manufacturing and CEA. Contract signed Jan 2026.
EDIH Ostrava
Collaboration with VŠB-TUO FEI on IoT, sensor networks, and cloud solutions since Mar 2024.
R&D Contract VŠB FEI
Dept. of Biomedical Engineering — measurement, data processing, cloud infrastructure. Control system operating in real environment.
HIVE Consortium
I3 project with 13+ partners across Europe. Our platform as the technological foundation of the consortium.