A few weeks ago I had coffee with a friend of mine who is a partner at a Toronto-based enterprise software company called Polyform Labs. Their products are geared towards the commercial real estate industry and, since I’m a big proponent of introducing more technology into the real estate space, I thought I would share a little bit about them with you all today.
The first of their 4 main products is called Lingo, which is a machine learning tool that helps companies quickly review legal documents and contracts. In the case of commercial real estate firms, the most obvious use case is leases. These documents are often hundreds of pages long and, if you have a big portfolio of properties, I’m sure you can imagine how quickly these pages add up.
What’s neat about Lingo is that you basically upload a lease and then the tool interprets and summarizes all of the clauses for you. It then tells you where you’re potentially exposed and where your risk factors are. And if by chance it gets it wrong, you can correct it and the system will actually get smarter – hence the machine learning part.
A few weeks ago I had coffee with a friend of mine who is a partner at a Toronto-based enterprise software company called Polyform Labs. Their products are geared towards the commercial real estate industry and, since I’m a big proponent of introducing more technology into the real estate space, I thought I would share a little bit about them with you all today.
The first of their 4 main products is called Lingo, which is a machine learning tool that helps companies quickly review legal documents and contracts. In the case of commercial real estate firms, the most obvious use case is leases. These documents are often hundreds of pages long and, if you have a big portfolio of properties, I’m sure you can imagine how quickly these pages add up.
What’s neat about Lingo is that you basically upload a lease and then the tool interprets and summarizes all of the clauses for you. It then tells you where you’re potentially exposed and where your risk factors are. And if by chance it gets it wrong, you can correct it and the system will actually get smarter – hence the machine learning part.
I’m not going to go through their entire product line, but I did also want to mention one more called Aura. They call it a “location-aware loyalty engine”. And you may have heard of similar products out there in the marketplace. What it does is use the MAC address on mobile phones (which is a unique, but anonymous, identifier) to track how people and crowds move through spaces.
The most common use case I’ve come across is within shopping malls and retail spaces. It’s used to determine which stores have the most foot traffic, which departments and aisles draw the most people, how long people stay in each store, where they buy, and so on. So even if you didn’t already know about this technology, you may have already been giving up your location data. There are lots of mall landlords using it.
And it’s producing some interesting data. For example, as soon as somebody buys one thing in a mall, their propensity to buy something else grows exponentially. This is what the data tells us and I can certainly relate to it from my own experience. So as a mall landlord and tenant, you are obviously trying to figure out ways to encourage people to make that first purchase.
The other use case that (obviously) came to my mind was with respect to city planning and urban design. How could we harvest anonymous location data to improve the way we design both our private and public spaces? Imagine if we had this data for subway stations, public parks, public plazas, and so on. I bet we would discover all kinds of ways to improve the experience within our cities. I’d like to see the location data for Trinity Bellwoods Park in Toronto on a sunny summer day.
But I digress.
If you’d like learn to more about Polyform Labs and their real estate products, click here.
I’m not going to go through their entire product line, but I did also want to mention one more called Aura. They call it a “location-aware loyalty engine”. And you may have heard of similar products out there in the marketplace. What it does is use the MAC address on mobile phones (which is a unique, but anonymous, identifier) to track how people and crowds move through spaces.
The most common use case I’ve come across is within shopping malls and retail spaces. It’s used to determine which stores have the most foot traffic, which departments and aisles draw the most people, how long people stay in each store, where they buy, and so on. So even if you didn’t already know about this technology, you may have already been giving up your location data. There are lots of mall landlords using it.
And it’s producing some interesting data. For example, as soon as somebody buys one thing in a mall, their propensity to buy something else grows exponentially. This is what the data tells us and I can certainly relate to it from my own experience. So as a mall landlord and tenant, you are obviously trying to figure out ways to encourage people to make that first purchase.
The other use case that (obviously) came to my mind was with respect to city planning and urban design. How could we harvest anonymous location data to improve the way we design both our private and public spaces? Imagine if we had this data for subway stations, public parks, public plazas, and so on. I bet we would discover all kinds of ways to improve the experience within our cities. I’d like to see the location data for Trinity Bellwoods Park in Toronto on a sunny summer day.
But I digress.
If you’d like learn to more about Polyform Labs and their real estate products, click here.
