Montreal Local Logic does AI for Real Estate

Local Logic is a big data company which uses a combination of geospatial and user generated usage data to quantify the qualities of any given location within a city. The technology developed by Local Logic is used to guide the decisions of e-commerce consumers when looking for real estate or travel accommodations and built predictive models to inform decision makers investing in urban environment.

We sat down with Naoufel Testaouni to get to know more about the company:


Who started the company? Do you / team members have tech background?

Alexandre Pagé, Colin Stewart, Gabriel Damant-Sirois and Vincent-Charles Hodder are the co-founders of Local Logic. Alex and Colin have a programming background while Gab has a background in statistics.

How are you being financed?

We have revenue from our existing clients and closed 2 rounds of financing from VC.

What do you think will be / is a big obstacle to overcome?

The biggest obstacle will be to scale our technology in geographies where the data we use in our algorithms are harder to collect.

How do you go about finding good developers / IT guys for your company?

We have the chance to build a product that can impact how cities are used and developed and most people find that project quite appealing. This helps us attract great candidates. Also, we’re trying to be involved in strategic community initiative and gain a good reputation and exposure through these.

Who is your biggest competition?

Walkscore is our main competitor. They offer Walkscore, Bikescore and Transitscore. They are based in Seattle and have been acquired in 2014 by Redfin an online real estate company. is another competitor that offers layers of information to add on maps in travel and real estate. Their algorithms use AI fed by social media feed such as Twitter and Foursquare. Based in Cincinnati, they offer their services to automotive industries to power autonomous vehicles.

How are you intending on taking your company to million dollars in
revenues? In what markets?

Our product is already available throughout North America and has good traction with major players in the Real Estate market. We also have big potential in the travel market, and in building predictive models for organizations investing in urban environment.

What is the big lesson you’ve learned (success or failure) with this

Putting in place a process that guides the tech and product team to build small iteration providing values to clients or other stakeholders, rather than converting new ideas into huge projects, allows you to build what is really needed and not waste months on the wrong thing.


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