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LOOK – It’s AI, It’s ML: It’s Designed Intelligence!

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How often have we all used the terms Artificial Intelligence (AI) and Machine Learning (ML) interchangeability? And is there anyone today who does not offer an “AI and ML and cloud and digital” solution that can solve every problem you have, and presciently evolve to provide for every foreseeable need you may have, even before it arises?

A quick glance on Google Trends for ML shows more than a four-fold jump for the phrase in the last 3 years. It is trending at an all time high!

Google Trends search for “Machine Learning”

First, let’s take a moment to delineate what Machine Learning exactly is. Machine Learning sits at the intersection of Computer Science and Statistics. In other words, you’re writing computer programs that operate on data, using dedicated programming languages such as R or highly specialized statistical packages such as SciPy for general purpose languages such as Python. These packages allow data-scientists to explore seemingly mundane data, extract shocking conclusions, and even make predictions.

At the heart of such programs lie one or more statistical algorithms called “models”, that are derived based on observations over a set of “training” data. For example, when training an image recognition program, the input may be images or pixels for a face and the output may be the emotion represented by that face. After several rounds of training, given a new face, the model can infer the emotion on that face, with some degree of confidence.

The quality of your input training data absolutely has a direct impact on the potency of your models, although some nifty math may help reduce noise in the data to some degree. The models may be comprised of various statistical distributions such as Logistic Regression, Bayesian classifiers, Neural Networks, or a combination of these. They try to best describe the input training data, and in turn, form the basis for predictions on new data.

So which models are best suited for the task at hand? Can new features be engineered or discovered from the training data and in turn, further improve the accuracy of future predictions? This is exactly where the science becomes art, since there are no set rules! One must apply observation, intuition, rigour, and experience to estimate the most accurate outcome.

We at Wysdom.AI can proudly stake claim to being a cognitive platform that serves a frictionless experience to our users and allow them to interact with systems the same way they would interact with other humans.

Each of these models abstract a lot of high school math – from probability to calculus. And yes, these models need constant fine tuning, since new input data may skew predictions since they represent new patterns that were simply never observed before. Or, they may be plain outliers, in which case, the business may choose to ignore them. Unaccounted emerging patterns in the atmosphere for example, skews weather predictions – and sometimes, by big margins!

Cognitive programs process large samples of data on an ongoing basis, and constantly infer patterns that human developers simply cannot manually recognize or code for. That’s exactly what makes these programs so unique and hence they grow smarter over time!

Three Peas in a Pod

So where does AI fit in? AI spans a much broader scope including psychology, linguistics, philosophy, neuroscience, robotics and not surprisingly, mathematics, to name a few. The goal of AI is to observe and mimic human behaviour and, of course, constantly learn from new data and patterns. In fact, ML is a specialized stream of AI and so is Natural Language Processing (NLP). NLP enables machines to read, understand and respond in spoken language, much the way humans do. NLP itself may utilize several ML algorithms, to understand one or more spoken phrases on hand.

As the lines between humans and machines blur, human to machine interaction powered by NLP, is an area of active research and ongoing improvements. Of course, it is rife with challenges, since language has theoretically infinite permutations and is always evolving. Albeit slowly (relative to our need for instant gratification), machines are definitely catching up!

Building AI systems take more than just data scientists. AI applications today must exhibit digital application paradigms such as web-scale engineering, 24×7 availability, infinite elasticity and enable seamless software updates at a very minimum.

At Wysdom.AI, our small team of highly talented developers, data scientists, linguists and DevOps engineers unite to deliver a truly turnkey AI platform for cognitive care. With expertise at its center and user-experience at its fore, we allow users to interact using spoken language over channels of their choice, understand what they really want and then serve them with the best possible solution – be it over self-service, social media or chatbots.

As our team now segues into deploying Deep Neural Networks, we at Wysdom.AI can proudly stake claim to being a cognitive platform that serves a frictionless experience to our users and allow them to interact with systems the same way they would interact with other humans.

Let’s usher in the era of “Designed Intelligence”!


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Data Scientist

As a Data Scientist, your day-to-day will involve writing queries, building dashboards, and preparing analytical reports about product performance for our clients and the Wysdom.AI team.  You will work with SQL, Tableau, and Python and ML Frameworks/libraries (among other tools) to create stunning visuals showing how Wysdom.AI is making their customers’ experience even better.

Implementation Engineer

As an Implementation Engineer you will be responsible for the solution integration of an enterprise grade conversational AI experience from a technical perspective. You will work closely with the lead solutions architect and be the technical face of the implementation team and lead the customer through the entire implementation cycle. You will work with one of the most diverse teams of linguists, data scientists, and innovators to deliver the best AI enabled customer experience.

Solution Architect

As a Solutions Architect within the Client Services team, you act as trusted advisor, responsible for the technical requirements and end to end solutions integration of Wysdom cognitive services within the client’s environment.  You will work with one of the most diverse teams of linguists, data scientists, and innovators to deliver the best AI enabled customer experience.

Cognitive Data Specialist

As a Cognitive Data Specialist, you will be responsible for the performance of the AI and quality of the corpus and will focus primarily on the VA training.  You will work with the client as required to ensure corpus is performing in an optimal manner.

Conversational Experience Designer

As a Conversational Experience Designer, you will be responsible for the designs of the overall customer experience, including the end-to-end dialog flows & journeys of the solution ensuring  design leverages  UxD best practices for optimal customer experience.

Conversational AI Specialist

As a Conversational AI Optimization Specialist, your responsibility will be to help drive the success of our solution for our clients. This involves building conversation flows, performing AI training, and partnering with clients to enhance their deployments.

Conversational AI Lead

As a Conversational AI Lead, you will be responsible for leading all Conversational AI program activities.  You will work with all team members to ensure deliverables are completed on time, with high quality and exceeds client expectations and goals.

Program Director

Responsible for the overall success for the client, including the end-to-end delivery and optimization of the solution, you will manage the sales process from pillar to post, including technical and commercial proposals, pipeline management, sales forecasting, and contractual documentation.

David Trotter, Wysdom

David Trotter

SVP, Sales & Marketing

David has 30 years of global sales leadership experience as a collaborative leader who believes in a strong team concept within sales and marketing organizations. David has spent many years working with growth companies and enjoys being face to face with customers and partners to create solutions that have a lasting effect on the customer’s business environment. 

Prior to joining Wysdom, David was the Vice President, Sales at Scalepad, and previously spent 11 years as Vice President for Latin America and Asia Pacific for Absolute Software. He also held senior sales management positions at GE Capital and Clevest.  

Michel Benitah

VP, Optimization & Delivery

Michel has 20 years of experience in leading the successful delivery of Conversational AI and Natural Language Customer Care solutions to some of the largest financial, telco, healthcare, utilities, and retail enterprises throughout North America. 


Prior to joining Wysdom, Michel spent 20 years at Nuance Communications, holding senior management and leadership positions within the enterprise division, most recently as director of the Toronto office and professional services team.

Frederic Lam

SVP, Sales

Fred brings in 25 years of international experience in sales and business development across North America, the Caribbean, Asia-Pacific, Europe, and the Middle-East.


Prior to Wysdom.AI, he held sales leadership positions at Oracle, Redknee, and Movius/Glenayre, successfully growing revenues in both large and small organizations. Fred has also been involved in the start-up community in the earlier stages of his career as an Investment Manager with SP Capital and was an alternate director on a few investee companies.

Karen Chan

Chief Engineering Officer, Co-Founder

With 20 years of experience in software and mobile, Karen has held senior technical roles at 5 startups, including Wysdom.AI, Clickfree, Mobile Diagnostix (HP), Teamatic, and Virtualthere.

Karthik Balakrishnan

Chief Technology Officer

Karthik has over a decade of hands-on, proven global expertise in emerging technologies and implementing intricate platforms and solutions for telecommunications and enterprise during his time at Amdocs, with senior positions in their India, Cyprus, America, and Canada offices.

Nitin Singhal

Chief Operating Officer

Nitin has over 20 years of success in global executions of business technology, driving operational efficiency and digital scalability for some of the world’s largest enterprise clients. 


Nitin spent 16 years at Redknee holding executive positions in Research and Development, Customer Operations, Partner Alliances, and most recently as COO.

Jeff Brunet​

President, Co-Founder

Jeff has more than 20 years of experience in the startup world, founding and growing 4 software companies: AracNet, Mobile Diagnostix (HP), ClickFree, and Wysdom.AI. 


His in-depth understanding of software development and the challenges in making new technologies successful in the startup world prove invaluable as he serves on the boards of XMG, SurfEasy (Opera), Locationary (Apple), Groupie, and as an advisor to Pushlife (Google), LogMeIn (IPO) and HP. 


Jeff holds 23 issued patents in the wireless and consumer electronics spaces and is the lead inventor on 30+ pending patents.

Ian Collins​

CEO, Co-Founder

Ian has founded and grown 6 technology companies over the past 20 years, primarily in the enterprise software space including Wyrex, Mobile Diagnostix (HP), Clickfree, and most recently Wysdom.AI. 


Ian invests, mentors, and sits on the boards of several startups in the Toronto area.