proprty.ai secures close to €130,000 from Innovationsfonden to take the CO₂ module international

Innovationsfonden has awarded proprty.ai €127,500 through the Innobooster programme to develop the next generation of its CO₂ module. Today the module only works in Denmark, on buildings with an existing energy label. The new version is a portfolio-wide compliance engine that runs on actual energy consumption data and works across European markets.

The grant is the second Innobooster proprty.ai has received. The first one, awarded in 2023, helped build the foundation of the product. This one extends the product beyond condition and maintenance into compliance, where new EU rules are putting growing financial pressure on property owners.

Compliance is now a capex question

Buildings account for around 40% of CO₂ emissions in Europe. The EU’s Energy Performance of Buildings Directive (EPBD) and the global CRREM standard are turning that into a financial risk for owners. Miss the targets and assets get stranded. Hit them the wrong way and capex balloons. Most owners do not have the data to know which path is cheapest.

proprty.ai’s existing CO₂ module already shows owners where they stand and what needs to change. But it depends on Danish energy labels, which means it only works in Denmark and only for buildings where a label exists.

“The current logic only works if you have an existing energy label, and it only works in Denmark. The new version will be built on actual consumption data. That means it can work outside Denmark, and it can give a much more precise picture of where you are and where you need to go,” says Ian Victor Magid Kjær, co-founder and CTO at proprty.ai.

What the new module does

Three things change.

The first is the recommendation engine. Instead of optimising for remaining lifetime, the new engine maps the most cost-effective pathway to stay compliant with EPBD and CRREM. It weighs remaining lifetime, intervention timing, kWh reduction, CO₂ reduction potential and cost, then returns the cheapest route at building, property and portfolio level.

The second is how the engine learns. proprty.ai is integrating real historic consumption data from public registers and customer systems. Predicted savings get compared against actual results, and the engine refines its recommendations based on what really happened.

“Today’s energy label reports carry an error rate around 60%. We are targeting an engine that lands under 15%. The way we get there is by training on real consumption data, not just predictions,” says Ian Victor Magid Kjær.

The third is where the recommendations land. Improvement activities flow directly into the work order systems property owners already use. Completion data flows back. Planned work, executed work and measured impact end up in one place.

“Compliance is going to be a real cost driver for property owners over the next few years. The owners who can map the cheapest path forward will protect cashflow and yield. The ones who cannot will pay too much, too late. We are building the tool that lets them choose the cheap path,” says Ian Victor Magid Kjær.

What happens next

The project runs from 4 May 2026 to 2 April 2027 and will be developed in collaboration with existing proprty.ai customers, who will validate parameters, contribute portfolio data and run the pilots. Customer participants will be announced separately.

proprty.ai uses domain-specific AI to predict building condition, remaining lifetimes and cost across large portfolios. The company works with property owners managing over 40 million m² across Denmark, Norway, Switzerland and Germany, and is profitable and past €1M ARR less than two years after product launch.

Thanks to Innovationsfonden for the grant, and to Nordic Innovators for helping us prepare the application.