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Applied Innovation

Innovative business visions and strategies are nothing if not implemented and applied to achieve real value for the intended stakeholders.

Too often, we have seen teams or organizations push for a big or bold vision, but when it comes time to deliver, the results are often modest, and fall short of the intended value proposition or ROI.

There are many factors that lead to these situations and it is through our Management Consulting offerings that we address these strategy and delivery challenges.

Our Applied Innovation engagements combine delivery best practices with the accelerated deployment of truly innovative solutions that unleash the untapped value of data and other business assets.

Each of our Applied Innovation engagements (6 to 12 weeks) typically includes:

  • A rapid diagnostic of the problem given current context

  • A target solution and actionable roadmap (people, process, technology)

  • A functional pilot or minimal viable product (MVP) that is ready to deploy and scale up across the business.

For example, we leverage Transactional Machine Learning (TML) for predictive business analytics on large volumes of fast streaming data in real-time. Another Applied Innovation example focuses on using the power of blockchain for secure information and document sharing to create the trust networks of the future.

Learn about examples of Applied Innovation in different industry sectors here.

Partners in Applied Innovation

We are delighted to have business partners who offer truly innovative business solutions that address some of our clients' top strategic opportunities.

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OTICS Advanced Analytics

OTICS is a pioneer of Transactional Machine Learning (TML) - a ground breaking solution that delivers deeper insights and more accurate predictive analytics on high volume real-time data streams at 50% of the cost of conventional ML. 

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myLaminin brings a new blockchain-enabled document and data sharing and verification service to market. 

We put people and businesses in complete control of their
data and respect their data privacy and sovereignty to enable the trusted networks effect of the future.

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SonarCX is a comprehensive All-In-One Customer Experience Management Platform that offers a central hub for all necessary tools. It streamlines customer interactions, ensures secure data management, enables efficient task tracking, and a centralized knowledge base. With simplified payment management, cloud-based accessibility, and mobile-friendly features, SonarCX empowers businesses to deliver exceptional customer experiences.

Here are some examples of tested high-value use cases using Transactional Machine Learning (TML), Blockchain and other technology innovations across major industry sectors.





Tourism &


Higher Education & Research

Financial Services

Enhanced client experience with secure sharing of credentialing and sharing of sensitive documents. 

For operational, risk management and regulatory compliance reasons, financial services clients are asked to prove their identity each time they open new accounts or access new financial products. However, in spite of investments in modernization, processes for client onboarding and service delivery are still siloed in many organizations.  Even the use of paper documents is still widespread for the sharing of sensitive information (e.g. ID photocopies, contracts, statements, proof of ownership, etc.).  Today's client service delivery model is often inefficient, labour intensive and not secure enough given today's cyber threats. 

Whether for banking, mortgage lending, investments or insurance - what if financial institutions had the benefit of assured inputs for Know Your Customer (KYC) and product applications? What if clients could manage their proofs of identity, other sensitive personal information and financial documents themselves in a secure blockchain-enabled digital wallet? What if they can securely share or receive documents from validated sources in a highly secure manner and in a more streamlined workflow that benefit from blockchain security and immutability?

They can! Talk to us about how blockchain-enabled innovation can enhance client experience, trust and prepare your organization for open banking opportunities.    

Securities Trading Surveillance

Transactional Machine Learning (TML) is a uniquely innovative technology that can accurately predict the probability of abuse, manipulation, fraud or other non-compliant  trading activity to mitigate losses, maintain trust and protect institutional reputation. Most surveillance tools offer key insights after the event has taken place. TML on real-time data streams can predict the probability of non-compliant activity in a manner that allows market surveillance and compliance professionals to proactively mitigate or avoid 'bad behaviours'.

This is achieved through unmatched depth of insights through autoML models that are auto generated at a granular level: by trading team, by trader, by exchange, by individual trade, etc. which results in millions of ML models that generate deep predictive insights.  

Transparency of Crypto Market Liquidity & Risk

2022 brought devastating consequences from the swift loss of confidence in global crypto marketplaces. As regulatory guard rails are being considered and institutional investors prepare to return after the market shake down (yes, they are), reliable insights on liquidity and risks in specific markets is a must.

We partnered with innovators to help institutional investors and regulators gain unprecedented transparency of crypto market liquidity and risks. This is achieved through real-time insights across all crypto exchanges (over 300 around the world) by processing millions of real-time trades in memory and generating autoML models at a granular level (e.g. by crypto exchange, by coin, by trading window). This results in dashboards of predictive trends in liquidity and market risks by exchange so that regulators can monitor and investigate and so that investors can make informed decisions about where best to place their trades.

Health Care

Medication Fraud Detection

The cost of medication fraud goes well beyond the financial losses. The illicit sale of highly addictive drugs has huge societal costs and a  devastating impact on the well being of a wide range of demographic groups. Transactional Machine Learning (TML) is a unique and powerful innovation to deliver predictive analytics on the probability of medication fraud detected from real-time transactional and other source data.

Population Health Monitoring

Monitoring changes in key health indicators for the early detection of potential illnesses in the population is a key objective for Public Health professionals and Researchers. Applying OTICS Advanced Analytics technology on real-time health data is a major innovation for the early detection of population health trends by tracking symptoms that may be due to: new  viral or bacterial contagion; ill effects of industrial accidents that release toxic materials; unforeseen long-term side effects of medical treatments or untested "fads"; or other situations that lead to harmful health effects in the population.  

Tourism & Lifestyle

Tourism is back and the competition for visitors is hotter than ever.

National, state and municipal governments have always sought to attract businesses and visitors to boost economic activity, investment and prosperity.  With the return of increasing demand for travel, adventure and different cultural experiences, tourism agencies are putting their best foot forward to entice visitors, domestic and international, to their region. These government tourism agencies need to sharpen their tools to compete more effectively and fulfill their mandates.

Their data strategy must now incorporate the use of AI for deeper insights and predictive analytics. The value of adopting a data-driven approach to achieve their key strategic objectives are significant: (1) improved decision making and competitive advantage, (2) insights on impactful initiatives to enhance visitor experience, (3) understanding fast moving developments to help local businesses thrive and increase economic upside for the region, and (4) greater campaign  effectiveness, to name but some of the major benefits. 

The added advantage of using Transactional Machine Learning is the ability to gain almost immediate insights from high-velocity real-time data at a fraction of the cost of conventional ML.


Higher Education & Research

Protecting Sensitive Research Data and Intellectual Property and Enabling Collaboration with Blockchain

Research-intensive institutions house a great deal of valuable research and innovation. Yet, because of their openness and collaboration, they have become ‘soft targets’ for IP theft. The management and sharing of research data poses many risks and challenges for these institutions, Principal Investigators, their teams, research participants, external collaborators, legal services, research librarians, and research ethics boards. Current solutions and practices are fragmented and inadequate, imposing significant operational inefficiencies.

Using blockchain technology offers an innovative way to provide this badly needed capability to protect sensitive content that is being shared among collaborators. The ability to securely store, and tailor access to research data for each team member and collaborator is also increasingly important to secure grant funding. Coupled with this, collaboration must be effectively coordinated to reduce operational inefficiencies and maintain ​a flexible agreement management protocol with eSigning capabilities. This is essential to reduce the overhead of these important activities on researchers and their teams.

Our partner, myLaminin, is at the heart of making this innovation a reality with leading research-intensive universities and institutions in North America. We also see huge value for this platform across many industries.  

More Examples of Industry Innovation

Oil & Gas: Optimized drilling to reduce energy consumption and protect high value equipment

In the pursuit of constant innovation and efficiency gains, solutions are sought to  fully automate key gaps that optimise the drilling process for greater efficiency and to extend the life of critical drilling assets by avoiding damage during drilling. Key questions can be addressed by the real-time AI-based recommendation engine such as:  when should I clean a hole? How long should I clean a hole? When should I back ream a hole?  What are the optimal settings to back reaming a hole?

Optimized weight on bit (WOB) and rate of penetration (ROP) resulting in avoiding damage to drill bits and faster time to production. By taking in real-time data from thousands of sensors, autoML models can be generated for each sensor for optimal algorithms for precise insights that guide decisions and actions. Immediate insights with predictive analysis were generated in 5 to 18 minutes. The practical outcome is a significant 20% reduction in the time to drill a well. Given the average or 30 days to drill a well, a savings of 6 days (at say $50,000/day) for each well ads up to significant savings in the cost of operation and reduction in the use of energy to complete the task.

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