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AI Supervision: Who’s The Bot Master?

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We spoke previously about the notion in AI – that failure to train is training to fail. The quality and nature of the data used to train AI algorithms, profoundly impact the performance and end-customer experience that AI based applications deliver.

Bots for long, have been the poster child of AI and have invariably fascinated one and all with their ability to interact using natural language and provide answers in a human-like manner, be it in the embodiment of home digital assistants such as Alexa or Google Home, chatbots hooked to proprietary chat platforms, or the more democratic Facebook or Microsoft bot frameworks.

Now that Virtual Agents are on every enterprise’s agenda, a few vexing questions arise:

  • What is bot training?
  • What skillsets do I need in order to train a bot?
  • Can my human agents and customer service representatives (CSRs), as part of their day-to-day customer conversations, train their virtual counterparts?

Machines have systemized components to perform what humans do instinctively – ask questions and resolve issues through conversations. Every AI bot has the following mandatory components:

  • Natural Language Processing and Understanding (NLP/NLU), which transcribes spoken or written phrases into their machine representation equivalents
  • Intents, as an outcome of NLU, tells the bot what the customer is asking about
  • Context, the often unspoken facet that actually enables triaging the issue and personalizing answers
  • Content, which comprises of phrases, articles or media returned by the bot

Natural Language Processing and Understanding leverage machine learning to transcribe natural language into a “semantic vector” that machines can process. NLP/NLU engines are typically trained by a specialized team of linguists and data scientists, who with their depth and expertise in language, develop models that lead to precise detection of Intents.

Language constantly evolves and so do the products and services offered by an enterprise. These can result in mismatched intents or “gaps” i.e. scenarios where the intent cannot be inferred. More so, even a simple bot can receive tens-of-thousands of questions each week.

Combing through such volumes and unlocking hidden patterns in data, needs specialized personnel with expertise in data science. NLP/NLU engines need ongoing upkeep, that include vocabulary expansion for newly launched offerings, semantic expansion for new diction or slangs and in some cases, a change to the underlying machine learning algorithms that better represent and detect the intents for the nature of questions customers ask.

Not all NLP/NLU engines are alike. Some perform better than others, due to the choice of the machine learning algorithms in play, the data used to train the engine and the training upkeep (or lack there of).

Context on the other hand, is derived by gathering customer data such as profile information, preferences, and device telemetry, etc. Context engines infer data that is relevant to the issue on hand, triage the issue to a resolution path and then personalizing the answer to the customer on hand. Context engines may take the form of API calls, mobile and web SDKs and are typically maintained by technical teams who are well versed with web & mobile platforms and integration technologies.

As for Content, once the Virtual Agent identifies the intent and the corresponding resolution path, it needs to return relevant content to the end customer using natural language. These span media such as images & videos, web-URLs that may point to support pages or deep-linked into self-service apps or pages, and the associated dialogues that mimic how humans would answer questions.

Content provides a great avenue for collaboration, where enterprises may leverage their own employees such as CSRs and business analysts, to augment their knowledge repositories with articles & solutions and also enhance their business practices and customer journeys. Good content can make or break the success of the bot’s adoption by end-customers.

Behind every successful Virtual Agent, is a diligent team that identifies emerging patterns in customers interactions and follows a “train ahead” practice i.e. proactively train the bot on an ongoing basis in collaboration with the enterprise’s personnel for appropriate content. It takes expertise, experience and collaboration to succeed after all.

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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.