An extensive range of AI technologies and applications to integrate into your business now.

Fields of Application

Cybersecurity

The smart city of the future will contain millions, if not billions of devices: all kinds of sensors collecting huge amounts of data about people and organizations, in real time. Machine learning algorithms can be the foundation of large-scale security monitoring solutions that have the capability to detect intrusions in autonomous systems, with very little need for humans. Artificial intelligence can also be used to automate all kinds of operations that can help secure those millions of devices, such as vulnerability management and response to attacks.


Insurance

In property and casualty insurance, modeling a client's exposure to adverse events typically relies on only a small set of variables. This causes some clients to pay too much, while subsidizing the truly risky individuals. AI generates more accurate predictions of extreme events with the available data, yielding fairer underwriting and ratemaking.


Financial ​Portfolio Management

It is both a science and an art to construct an asset allocation that meets the investment objectives of a client while respecting risk and exposure constraints. New developments in operations research coupled with advanced forecasting can now tackle asset-liability optimization to generate the investment returns that meet future needs, even in adverse market scenarios. These management techniques can be scaled from the individual retirement account to an entire pension plan.


Risk Management

Risk estimation and forecasting has a long history in econometrics. All financial institutions use risk models to manage their market exposure and capital requirements; and advances in nonlinear time series models and recurrent neural networks are multiplying the types of risk factors that can be considered, resulting in finer estimations with more predictive power.


Predictive Industrial Maintenance Solutions

Industrial engineering and operations research have long modeled wear and tear on industrial equipment to plan and order replacement parts. AI can now interpret high-frequency time series from distributed sensors to make timely predictions of imminent failures, instead of relying on long-run averages, thus enabling a large material reduction in equipment downtime.



Automated Decision Making

Decision support tools have been around for years now, but AI takes it to the next level by independently determining how to adapt to an ever-growing number of circumstances. As situations grow in complexity, so must technology, and AI helps decision makers spend more time solving problems that require the most creative thinking.


Computational Advertising

An unprecedented amount of fine-grained, behavioural data is now available for advertisers to fine-tune their campaigns. Artificial intelligence makes this data actionable and relevant while preserving customer privacy and experience. In contexts where the decision volume can be very high (in the millions of predictions per second) within very tight cost and economic constraints, specialized expertise is required to create measurable economic outcomes operating in real time.


Consumer Engagement

More and more information is available for you to dissect and anticipate the needs of your customer. You can now leverage AI to gain new insights. Though, more importantly, you can use it to be proactive and align your business to take advantage of an increasingly dynamic market with information from a multitude of internal systems, third-party databases and social media.


Multichannel Customer Care / Contact Centres

Deep learning has been profoundly changing our ability to understand language, semantics, speech recognition and related applications. Contact centres can now leverage this technology in order to better understand their operational dynamics, resolve issues, and most importantly, augment agents to help them reach superstar status.


Self-Driving Vehicle AI Components

Many challenges lie ahead in the realm of self-driving vehicles, but AI is already solving a multitude of problems that would have been unthinkable a few years ago. AI can now see, hear and navigate in complex environments, broadening the horizons of autonomous machines.


Creative Tools for Special Effects & Video Game Design

The progress of deep learning technology has unlocked our ability to reshape and create unprecedented multimedia experiences. From emulating someone’s voice, to dynamically editing videos, AI allows us to create almost any illusion in record-breaking time with little effort.


Industrial Manufacturing

The cyber/physical revolution is happening now and AI opportunities in manufacturing have never been greater. Next generation quality assurance systems, predictive maintenance and planning are all becoming available due to increased sensors and computing power. Manufacturing and AI can lead to smarter production, more flexibility, improved turnaround, reduced defect rates and downtime, all the while increasing quality.


Supply Chain Management

With demand, prices and costs that are constantly fluctuating for all of your operations, a good plan and solid processes are no longer enough. AI is the only way to stay competitive by continually sensing, reacting, assessing results and learning from successes and errors. AI will improve your quality of service while significantly reducing overall operational costs.


Algorithmic Trading

For decades, traders have relied on algorithms to execute complex orders on financial markets. Now, AI is supercharging those algorithms, sensitizing them to a broader range of explanatory factors, from macroeconomic conditions to market sentiment. This gives traders a more dependable edge, at lower cost.


Robotic Process Automation

Legacy computer systems are often best integrated by having a software robot simulate data entry at the user interface level; but, most of these automation tools still require rigorously hand-coded rules. Artificial intelligence allows these robots to learn and improve their own code using past examples, minimizing the time to develop.


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Technologies

Speech Recognition

In the past few years, the accuracy of speech recognition systems — converting speech waveforms to a word transcription — has progressed by leaps and bounds thanks to deep learning. Just 10 years ago only short sentences with a simple vocabulary could be recognized. Today, state-of-the-art speech recognition systems can reliably transcribe continuous speech with a wide vocabulary, including that of speakers with foreign accents.


Advanced Recommendation Systems

Offering shoppers a set of items that they are likely to want to buy right now is the key to high conversion rates in e-commerce.  Impactful recommendation systems must go beyond “users also bought....” They need to be attuned to a fluctuating set of diverse factors including recent behaviour, product properties, unknown shoppers and the daily market.


Advanced Forecasting Systems

Time series forecasting is central to an efficient and robust supply chain; yet, the state of practice remains largely confined to short-horizon forecasts with relatively low predictive ability. Advances in recurrent neural networks are enabling much more powerful capabilities, ranging from sharing information across groups of related items to incorporating the impact of complex causal factors.


Image Recognition

Computer vision has perhaps provided the most outstanding example of the progress in deep learning over the past five years, with natural object classification reaching superhuman performance in 2015. Broad industrial applications are now accessible using models for related tasks such as object classification, object detection, semantic segmentation and instance segmentation. These capabilities can be made part of larger pipelines enabling, for instance, answers to voice searches for pictures or product feature comparisons.


Natural Language Processing

Recurrent neural networks are behind one of the most surprising leaps in natural language processing abilities, launching a new standard for the state-of-the art in language modeling and natural language translation. One such breakthrough is the ability to convert spoken sentences into numerical values, allowing a host of analytical techniques to be applied to these data with great efficacy.


Predictive Modelling

As an analytics discipline, predictive modeling tries to anticipate scenarios that have never happened before: "Is a client likely to buy this service?" "What claim will the client submit if her new house burns down?" AI has improved on traditional predictive models by making use of vastly more data and richer explanatory variables (such as images and natural language text), enabling much more nuanced characterizations of future behaviour.


Interested in learning more about how these technologies will impact your business?