Agilias was not born as a software experiment looking for a market.

It grew from Agrovent’s engineering work with real production sites — where climate systems, equipment, people, biological processes and business economics meet every day.

For years, Agrovent has designed and delivered microclimate systems for vegetable storages, greenhouses, poultry houses and livestock facilities. This work has shown a recurring gap that is not solved by a controller screen alone.

A controller may show that the system is working. A daily report may say that everything is normal. But the site may still be losing money, product quality, energy efficiency or production stability.

A production site with a digital twin layer connecting equipment, climate and operational data

Agilias is designed as a digital layer above existing facilities, not as a replacement for every controller or automation system.

Why Agrovent is opening a separate technology direction

The real operational questions are usually deeper than a simple alarm:

Why does one building perform worse than another building with similar equipment?
When did the site begin drifting away from the optimal operating mode?
Which equipment is overloaded, underused or close to failure?
Where do hidden losses appear before they become visible in reports?
How are climate, equipment, video, events and human decisions connected?

These questions require a different layer: one that can connect field engineering, operational context, historical data and modern analytics.

Agilias: domain-first Industrial AI

Agilias is a separate technology direction focused on digital twins, operational analytics and Industrial AI for real-world production sites.

The important difference is not only technological. It is methodological. Agilias does not start with a dashboard and then search for a use case. It starts with operational questions that owners, engineers and production teams struggle to answer.

Field engineering work connected to a digital twin model through equipment and sensor data

Domain knowledge matters because the same sensor value can mean different things depending on equipment layout, biological process, season, operator action and economic context.

The goal is not to create another dashboard. The goal is to turn fragmented operational signals into practical engineering and management decisions.

What Agilias connects

Equipment signals

Fans, motors, dampers, heaters, cooling units, valves, alarms and operating hours.

Climate and process data

Temperature, humidity, CO₂, air pressure, airflow, storage conditions and production-cycle context.

Video and visual signals

Behaviour, movement, use of space, process anomalies and visible deviations where cameras are already installed.

Business context

Energy use, maintenance cost, product loss, quality risk, downtime and management reports.

Where it can be applied first

The first focus areas are close to Agrovent’s engineering experience:

poultry and livestock houses;
vegetable, fruit and potato storage facilities;
greenhouses and controlled-environment agriculture;
industrial sites with distributed equipment and operational risks.

But the logic is wider than agriculture. Any physical business that depends on equipment, people, energy, climate, quality and time-sensitive decisions can eventually become a candidate for this kind of digital layer.

A digital site model highlighting hidden losses, equipment load and decision points

The practical value appears when a digital model helps a team discuss specific zones, equipment loads, weak signals and decisions instead of reviewing disconnected screens.

How Agilias differs from a standard IT product

Typical software-first approach	Agilias domain-first approach
Start with available data and build dashboards around it.	Start with the operational question and collect only the data needed to answer it.
Treat equipment, cameras and reports as separate integrations.	Connect them around one site model and one decision process.
Focus on interface, AI features and platform architecture first.	Focus first on where the site loses money, stability, quality or time.
Assume digital transformation means replacing existing systems.	Begin above existing infrastructure where possible: controllers, cameras, logs and reports.

The first practical route: pilot before platform

Agilias is being built around real facilities, not abstract AI promises. A reasonable first step is not a large transformation project. It is a focused pilot with one site, one decision problem and a measurable operational question.

Define the production problem and expected economic effect.
Identify existing data sources: controllers, cameras, logs, reports and operating events.
Build a limited digital model for the selected site or process.
Deliver findings that can be checked by engineers and managers.
Decide whether the pilot should become a permanent product deployment.