Top-10 Artificial Intelligence Startups in Canada
“Canada eh, they started a country and nobody showed up”, Rodney Dangerfield once quipped. He was probably referring to the fact that Canada has the second largest land mass of any other country in the world, but only 36 million people living there (about the same number of people living in California.) The difference of course between Californians and Canadians is that Canadians have a sense of humour, and they know how to spell it.
In charge of all that land is a man famous for his boyish good looks and firm handshake, Justin Trudeau, who is leading the 18th largest country in the world by GDP and winning lots of friends by letting everyone there smoke weed. In 2017, his government pledged $125 million for a national artificial intelligence (AI) strategy which aims to increase the number of skilled graduates and researchers in the field of AI, and establish cities like Edmonton, Montreal, and Toronto as research hubs for artificial intelligence. While U.S. companies may be trying to steal Canada’s top AI talent, it hasn’t stopped Canada from birthing a large number of AI startups, some of which we looked at in our article on “9 Canadian AI Startups Making Canada Great Again”. In this article, we’ll look at the ten most funded AI startups in Canada.
We’ve first looked at this “AI-as-a-service” offering about a year ago, and since then lots has happened. Founded in 2016, Montreal startup Element AI has received a whopping $102 million from the likes of Microsoft Ventures, Intel Capital and Nvidia to create a platform that marries academic AI research with real-world business implementations.
Rather than offering out of the box solutions, the company engages with clients by developing a sector and client-specific roadmap to implement AI algorithms into each business, then executes these projects on a case-by-case basis aiming to maximize return on investment (ROI). The team has been busy since their huge investment arrived last summer, opening new offices in London and Toronto, and going on a hiring spree. It’s now the largest privately-owned artificial intelligence R&D lab in Canada.
Founded in 2013, Toronto startup Rubikloud has raised $45.5 million to develop a suite of software-as-a-service products for the retail industry, with Intel Capital as a lead investor. The solutions include a promotion manager and a customer lifecycle manager, a big data platform that hosts machine learning applications, and a machine learning library. These tools promise to double sell-through rates and provide accurate forecasts for campaigns, decrease marketing overhead by 50%, and improve sales by 10%.
Retail enterprises can take all the data they already have, plug it into the big data framework, and the AI algorithms figure out the best course of action. The A.S Watson Group, a Hong Kong-based retailer of health and beauty, operates 13,000 stores in 25 markets. After a 10-month pilot, Watson Group reported an 8% increase in campaign sell-through rates and has since expanded Rubikloud’s machine learning suite across their entire company.
Founded in 2011, Toronto startup Maropost has raised $37 million in funding to develop their own take on AI-based marketing and sales platforms. Taking on CRM software giants like Oracle or Salesforce, Maropost’s applications make giant promises, like a 63X return on investment for your marketing spend.
The team has a solid client base including some high-profile names like Rolling Stone magazine, the New York Post and Mercedes Benz Canada. The latter reported 3x the click rates and 4x the open rates of their targeted marketing collateral compared to the industrial average thanks to the new solution. Maropost grew in the early stages by word of mouth but is now scaling rapidly, having doubled its workforce to 150 employees in 2017 and having been named Canada’s 3rd fastest growing company by Deloitte.
Founded in 2012, Toronto startup Analytics 4 Life has raised $29 million in funding to develop a new medical imaging technology for cardiac diagnostics using AI algorithms. The application maps patients’ heartbeats using a visualization technique called Phase Space Tomography which is then analyzed by algorithms and forwarded to a doctor.
The imaging device is being developed specifically for the monitoring of coronary artery disease, and unlike other methods, there is no need for radiation, heart rate acceleration, or injections of contrast agents. The company’s machine learning algorithms are currently in the clinical trial stage, and are being tested in 13 hospitals in Canada and the U.S.
Founded in 2015, Ottawa startup Interset has raised $24 million to develop a cybersecurity solution based on machine learning. That’s nothing new, since we’ve talked about quite a few firms using AI for cybersecurity. In a familiar story, Interset’s tool is built on an open big data platform that is scalable to the size of each client. Unsupervised machine learning algorithms use all that delicious big data to look at the context of a threat to arrive at a conclusion, reducing false positives and flagging high-risk threats without the need for humans. Interset’s marketing team did a good job of dedicating an entire page to the inevitable “how are you different” question which can be summarized as follows:
After extensive testing, the U.S. intelligence community determined that Interset’s visionary architecture can achieve threat detection that’s faster and more accurate than any other analytics-based product.
Being better than everyone else is a good start. The company’s strategy is to connect and augment existing security systems like data loss prevention, endpoint detection and response, and identity and access management. In most cases these are separate applications that don’t talk to each other, making it impossible to view cybersecurity holistically and prioritize threat levels. Interset adds an overlay that orchestrates all cybersecurity operations and fills gaps, so clients don’t need to remove and replace current systems that cover certain areas fine.
Founded in 2012, Montreal startup mnubo has raised $17 million to develop an Internet of Things (IoT) analytics solutions for consumer products and industrial assets. Clients can use mnubo’s software-as-a-service platform to receive close to real-time information on the usage and state of connected products ranging from coffee makers to mining machinery. It all boils down to a comprehensive view on product usage, which in turn translates to increased customer engagement and stickiness, predictive trends, and better product development.
Sensors in your washing machine tell its manufacturer which programs you use, when, and how frequently, for example. This information allows for predictive maintenance and might be useful to product managers as well. In industrial settings, companies can maximize utilized capacity, like scheduling repairs on car assembly robots and arranging replacements weeks ahead of the needed repairs. Mnubo also offers IoT consulting for companies looking to develop their IoT strategy from scratch and provides the machine learning framework to make sense out of all your delicious big data. The company recently opened an office in Japan, despite the fact that not a single person in the entire country will be able to pronounce “mnubo”.
Founded in 2014, Toronto startup Deep Genomics came across our radar a few years ago when we wrote about how they were “applying deep learning to gene editing“. The startup has raised $16.7 million in funding from the likes of Khosla Ventures to create an AI platform for gene-based drug development, using deep learning to analyze genomic data and identify genes responsible for certain diseases, then building drugs to address the behavior of these faulty genes. Their team has built a “library” of tens of billions of chemical compounds that can be searched efficiently using their algorithms, and which, based on their qualities, might become drug candidates. Current research is focused on genetically defined metabolic and neurodegenerative disorders (these happen when neurons lose their function or die, like in the case of Parkinson’s or Alzheimer’s). Deep Genomics has also teamed up with Wave Life Sciences (WVE) to explore drug candidates for the treatment of neuromuscular disorders that impair proper functioning of muscles.
Founded in 2012, Toronto startup Statflo has raised $14.4 million to develop a sales acceleration tool combining big data analytics and human sales coaching. At implementation, Statflo’s data engine imports sales and customer relationship data, removes duplicates, and categorizes customer action items. The output is a so-called Smart List that gives the retailer a list of clients that are “low hanging fruit”. Client calls can be made by sales reps in their free time or can also be handled by Statflo’s Customer Success team.
The combination of AI analytics and live sales coaching results in double-digit sales increases in 60 days and a minimum of four times return on investment (ROI), Statflo claims. The company specifically targets wireless and technology retailers at the moment. The application interface looks like a step-by-step sales coach for dummies, something that may come in handy at offshore call centers.
Founded in 2011, Edmonton startup Granify is another company helping e-commerce retailers maximize sales using big data, something we touched on before in an article last year titled “7 Examples of AI in Retail and e-Commerce“. Granify has raised $13.5 million in funding so far to develop a solution that monitors the minute details of customer behavior on websites like products viewed, scroll speed, and mouse movements to come up with an optimal customer journey and automatically handle potential objections of customers. When a prospect wants to leave the website, Granify sends a message to him or her handling the most probable objection.
The platform analyzes more than 400 data points per second in real-time and claims to deliver a 3-5% increase in revenues within 90 days of implementation by enhancing conversion rates. Considering that a big part of an e-commerce marketing budget is spent on obtaining traffic, the results achieved by Granify directly strengthen a retailer’s bottom line. Granify charges for their product based on performance fees, which shows how strongly they believe in their product, and which helps reduce those pesky “cost objections”.
Founded in 2015, Waterloo startup ApplyBoard has raised $13.5 million to develop an AI-assisted online marketplace for international high school, undergraduate, and postgraduate applications. The company’s platform analyzes applicant profiles including academic background, desired studies, and financial situation to recommend the best matches for each student. Thanks to this vetting process, ApplyBoard boasts an impressive 95% acceptance rate among the 10,000+ students who used the service so far by answering six questions.
ApplyBoard currently offers positions in more than 750 schools in the US and Canada, and was founded by a group of international students facing the exact challenges they are trying to alleviate now, and is getting lots of positive feedback from students and institutions alike for their great communication and customer service skills. It’s great to see international startup success being built on a valuable cause like education – very Canadian.
UPDATE: 7/27/2018 – When we first published this article, we questioned as to whether AI was needed for the ApplyBoard business model. It’s a fair question, and ApplyBoard called us on it. After some email dialog, they disclosed that “ApplyBoard uses a combination of various data including all historical admission data from partner schools (750+) and visa data.” That’s a lot of big data to analyze, which is all happening in the Amazon Cloud. This resulted in two things happening. One, we removed our snarky comment, and two, we wrote an article about transparency in AI which is worth a read.
So there you have it, ten Canadian AI startups with the most funding to date, 40% of which are dabbling in the retail space. As Bono once said, “the world needs more Canada”, and it looks like that’s what they’re going to get.
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