The Real Estate
Data Problem

As industries adopt data-driven products, they seem to follow a particular path. First they have to aggregate enough data to actually analyze, then they start leveraging business intelligence products that use that data, then they pursue artificial intelligence trained on that data for better decision making.

To analyze anything, you first need a large volume of well-structured data. In Canada, the real estate industry is still in the “data collection” phase because it has few widely recognized sources or standards. The growth of the real estate tech industry has accelerated, and best-in-class point solutions are beginning to lay the groundwork here.

After the data is flowing, you typically see the development of business intelligence products to deliver insights from this data. Real estate in Canada is at the doorstep of this phase. But bringing the data in-house, to drive business intelligence may take a while. At least that's what consultants pushing Power BI solutions would estimate.

The last phase is using AI and machine learning to inform decision making. The industry hasn’t achieved broad adoption of this technology. The bulk of algorithms used in real estate are for data extraction and organization, because we’re still working on the problem of bringing all our data together, in house.

The Solution: Open the door
for AI Use-Cases

When we first set out to launch Divisin, we bought an .AI domain and tried to come up with a revenue management solution that would scale. Unfortunately, the data available was too sparse to develop a proof-of-concept. We didn’t want to attempt another “all-in-one” point solution that partially solves problems.

The way forward for us was “end-to-end.”

First, we aggregated the most accurate, complete, and organized foundation of data possible. And we continue to work on improving the integrity of the datasets and getting them to “talk to each other” so real estate companies can analyze their own data alongside a high quality, shared-data ecosystem.


This single source of truth enables consistent, data-driven decision making across the entire organization. Instead of building another dashboarding platform for people to navigate, we chose to focus on data engineering and data science in order to bring the benefits of AI to our clients.


There are way too many products with overlapping features, too much complexity, too little time to figure out how they work. Instead of going down this path, we decided to solve the data problem, and to make it extremely easy to access with AI Assistants.


We minimize work and maximize value by leveraging AI to solve complex problems with accuracy and deliver the benefits of AI — speed, efficiency, and scale — to your organization.