Top-10 Artificial Intelligence Startups in Canada
Top-10 Artificial Intelligence Startups in Canada
In 2017, Canada 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.
Credit: Analytics 4 Life
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:
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.