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AiThority Interview Series With Kerry Liu, CEO at Rubikloud

Tell us about your journey into Artificial Intelligence? What made you launch an AI-powered retail automation platform?

Large enterprise retailers around the world consume huge amounts of data from their customers with no way to truly leverage it because their cloud solutions aren’t compatible with their legacy systems.  Before founding Rubikloud in 2013, we noticed a large gap in the market that demanded the implementation of AI and machine learning software and that there were very few companies with the solutions that Rubikloud provides across all verticals, let alone specifically for retailers.

We built solutions for retailers to port their data to one place, clean it and standardize it across their digital and offline channels. In the process, we realized that data had to be warehoused on a sophisticated modern cloud. We believe embedded machine learning, industry-specific decision engines, and elastic cloud compliant architectures will be at the forefront of all future enterprise software systems.

Rubikloud’s solutions are unique and often an entirely new concept for retailers to understand. Rubikloud challenges the stereotypes associated with AI by championing Rubikloud’s platforms and solutions as a way to break free of legacy in-house systems and help retailers make meaningful business decisions effectively, resulting in customers making easier and more relevant purchases.

What are the foundational tenets of your intelligent decision automation platform?

Rubikloud is the world’s leading data and machine learning (ML) platform for retail. Our full-stack, cloud-native platform and two flagship applications automate and improve mass promotional planning and loyalty driven marketing for our multi-billion-dollar retailers.  Rubikloud’s big-data architecture gathers retailer data from both legacy and new, online and offline systems. This data is then used to help retailers make tangible actions that grow their loyalty revenue, promotion forecast accuracy, and merchandising profitability. We work with leading omnichannel retailers around the world.

What is the ‘AI in Ecommerce and Retail’ in 2018-2020? How can SaaS platforms better benefit from Rubikloud?

Our cloud-native, product-centric approach to development has enabled us to overcome often difficult enterprise deployments of mission-critical solutions. To minimize effort and resources for our clients, we have developed tools to speed up and reduce the costs associated with the onboarding of new clients onto our products. These tools significantly reduce the time and resources it takes to ingest and control the quality of client data, configure the product to the specific needs of the client and launch the product for full client use, by almost half.

What does your ‘Ideal Customer’ Profile look like? Which new geographies are you currently targeting?

Rubikloud’s solutions are designed to address business challenges in merchandising and marketing teams for enterprise level retailers (typically they are multi-billion-dollar retail operators). The core markets we serve are North America, Europe and Asia-Pacific (APAC).

Rubikloud works best with enterprise retail customers with data sets in place that can be analyzed and optimized. However, our “ideal customer” is really any retailer or enterprise who believes in the transformative power of AI and can derive value from our retail-specific AI solutions.

How do you make AI deliver economic benefits as well as social goodwill?

In terms of economic benefits, Rubikloud’s use cases for promotion planning, loyalty management, and inventory forecasting is mission critical to retailers and can generate 10+% revenue and profit gains for retailers.

In terms of social good, we are redefining the shopping experience for consumers. An exceptional customer service experience is crucial but shouldn’t be overthought. Most customers want to buy from a company that is approachable, understanding, and feels human. Rubikloud helps bring this personalization through AI.

How do deep learning algorithms, speech recognition and natural language processing technologies converge at Rubikloud?

For our use cases, we’ve found that deep learning algorithms are primarily useful in two key areas:

CNNs that help in deconstructing complex features spaces that we leverage as part of our recommender systems; and,

RNNs that are very useful in decomposing time-series data which drive higher accuracies for our forecasting and inventory-allocation systems.

While these techniques are commonly associated with speech recognition and computer vision related use cases, we’ve effectively applied them to higher impact financial problems that our enterprise clients desperately need to be solved.

What’s the synergy between A.S. Watson and Rubikloud’s AI platform?

Rubikloud is a strategic partner of A.S. Watson, the largest international health and beauty retailer in Asia and Europe. In 2017, A.S. Watson doubled down on their big data capabilities with this partnership as well as a $70 million investment (mostly received by Rubikloud) on big data, to further enhance customer experience and operational efficiencies using machine learning and data visualization applications.

A.S. Watson partnered with Rubikloud to deploy RubiCore, the company’s data enterprise platform built for easier artificial intelligence applications plug-in, as well as two of its machine learning applications, namely Promotion Manager and Customer LifeCycle Manager, in A.S. Watson’s network of 13,300 retail stores across 25 Asian and European markets.

What were the key takeaways from the recent Founders Forum?

Founders Forum provided a valuable environment for no-fuss, straightforward networking where founders could get right to the point on the topics they wanted to discuss. That kind of candid, person-to-person engagement was refreshing and beneficial to everyone who participated.

Tell us about your AI research programs and the most outstanding digital campaign at Rubikloud or elsewhere?

Rubikloud takes a holistic approach to research. We combine our internal AI talent with academic and government-funded PhD students from Canada’s top universities to pursue our AI research agenda. Our research interests are in a wide variety of topics, from building clever solutions to data-sparse conditions, to finding novel solutions to much lower-level problems such as automating the way data is cleaned, labelled, and partitioned across distributed systems.

What are the major challenges for AI technology companies in making it more accessible to local communities? How do you overcome these challenges?

Market evangelism. AI is everywhere in the media (autonomous cars, robots, etc.), but its context and relevance to most companies get lost in the hype. Most companies have been looking to traditional technology companies for leadership for the past five years with no results. We will be able to better reach local communities by trying to redefine the narrative around AI and showcasing means for the enterprise.

What AI start-ups and labs are you keenly following?

Because we’re focused not just on generic research interests, but on the effective operationalization and robustness of production-grade AI systems, we look at companies like Blue Yonder and Bonsai.

What technologies within AI and computing are you interested in?

Any tech where there is a practical application for the workflow of a big enterprise.

As an AI leader, what industries you think would be fastest to adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology markets?

Industries where customers already have expectations for personalized service and a faster pace. Industries like banking and telco, where technology has already been integrated to a certain level; the infrastructure is already in place and customers expect them to keep pace with the latest advances in tech to create more personalized experiences they can deliver even faster.

What’s your smartest work-related shortcut or productivity hack?

Hire people better than you!

Tag the one person in the industry whose answers to these questions you would love to read:

Jeff Bezos

Thank you Kerry! That was fun and hope to see you back on AIthority soon.

I’m currently the CEO of Rubikloud, Canada’s most disruptive enterprise start-up. Prior to Rubikloud, I led the channel sales team at Strangeloop Networks, a global leader in web performance optimization. Strangeloop was acquired by Radware in early 2013. Prior to Strangeloop, I spent 3 years in various roles within PricewaterhouseCoopers focusing on audit, strategic consulting, and M&A integrations in the technology sector.

rubikloud Logo
Rubikloud empowers retailers to leverage machine learning and big data systems in meaningful ways.

Founded in 2013 and based in Toronto, Rubikloud is an artificial intelligence (AI) company that has created the world’s leading, cloud-native, machine learning platform for retail. We started with three guys, and a puppy named Charlie, on a very beige couch in a very small apartment.

Fast-forward to today and the couch and apartment are long gone. We’ve grown quickly to now include nearly 100 Rubikrew with expertise in AI and machine learning, enterprise software engineering, retail product development, and retail customer experience.

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