AktivBo launches AI service for the real estate industry

Proptech company AktivBo has developed an AI service that helps real estate companies achieve more satisfied tenants while saving both time and money. Using AI and ML as support, tangible actions are presented together with cost and time estimates for implementation, as well as estimated cost savings (ROI). The development has taken place together with selected real estate companies where tenants' feedback from customer surveys has been used as a basis.

Industry-specific AI based on 30 years of experience 
When developing the service, AktivBo has benefited from data and experience acquired over a period of 30 years. Knowledge of how real estate companies work and implement improvements linked to customer satisfaction has been key for the ability to develop a highly reliable service. The solution uses the latest technology for generative AI, which enables the AktivBo service to classify large quantities of tenant feedback in an effective, structured manner. The feedback is fully anonymised in order to preserve the tenants’ privacy. It is transferred and stored in encrypted form, is not used as training data, and is automatically deleted after classification. 

- The ambition of the AI service is to further streamline and support the real estate companies' improvement work. Major advances have taken place in AI technology in recent times and most industries have jumped on the bandwagon in order to create new, more efficient automated services. However, the systems and new services will only be as smart as the data we give them. Thanks to our acquired experience and knowledge, we can now create services of this type, which add real value and show specific ROI. Our new service will be continually developed to keep pace with the general development of AI and is just one of several exciting investments we are making in the area, says Martin Talme, Product and Business Manager at AktivBo. 

Scalable model with great potential for real estate companies 
The service that has been developed is scalable and works for different types of processes in a real estate company’s customer journey. It can also identify actions for both housing and commercial real estate companies at different data levels with drilled-down possibilities. Suggestions for specific improvement actions are provided automatically based on tenants’ written responses, and are then categorised according to business area and type of action. The time and costs for the performance of the action are then estimated on that basis. That means that it becomes an important link between the analysis work and the action planning that customers link to the tenants’ responses.  

- Every year, we collect over 1.2 million text responses from different types of surveys, and we chose to use these as a starting point for the development of the service. There is great value in the tenants’ own words which contribute towards understanding, and this is not fully utilised in the improvement work. This becomes powerful when combined with quantitative responses and makes monitoring and improvement of the entire customer journey more efficient. In that way it will benefit both tenants and landlords, and several companies have expressed an interest in being involved in the further development of the model, concludes Martin Talme.