Operational AI at the Edges

Our mission is to innovate at the forefront of emerging AI LMs and Quantum Computing (QC) technologies, driving progress by creating transformative solutions that make operational processes more efficient, accurate, and sustainable. Through collaboration we co-create innovative and transformative solutions that maximize benefits and promote sustainable operational processes and actions, including climate actions.

OperAI uses artificial intelligence (AI) and quantum computing (QC) to improve manufacturing and business operations by integrating AI and QC into operational processes. It enables task automation, efficiency improvements, and data-driven decision-making, from planning decisions to the implementation within a sustainability perspective. OperAI uses a dual approach to improve these operations.

OperAI works with strategic partners to help achieve both operational efficiency and operational sustainability by streamlining and speeding up operational processes within a sustainability perspective, combining emerging technologies, including quantum technology. OperAI uses a dual approach to help develop and implement innovative and transformative solutions addressing productivity and climate change. Previous solutions have been considered as breakthroughs spanning digital process automation (DPA) to rapid discovery. Processes that helped in shortening the time and reducing costs while empowering and unlocking creative potential.

Climate change has created unprecedented challenges requiring to speed up adaptation and mitigation processes across sectors, including agriculture, energy and transportation. Climate change impacts and risks are becoming increasingly complex and more difficult-to-manage. Between 34-39% of companies abandon their efforts to address such complex and diffcult-to-manage problems. Comprehensive, effective, and innovative responses can harness synergies and reduce trade-offs between adaptation and mitigation to advance sustainable development. OperAI is developing innovative tools, including development of transformative tools to help address complexity and accelerate the transition towards more sustainable and efficient operations.

Source: IPCC Report (2023)

Urgent climate actions needed to avoid tipping points concluded a recent study. It also points out that limiting warming to well below 2°C and preferably 1.5°C is not safe as 1.5°C, a goal that was made during the 2015 Paris Agreement on climate change by the year 2030. A global temperature above 1.5°C may trigger multiple climate tipping points.

We have been working to develop some of the tools than can help to address and speed-up some of the climate adaptation and mitigation challenges by proposing hybrid enhanced operational tools which leverage the power of classical or/and quantum emerging technologies.

The location of climate tipping elements in the cryosphere (blue), biosphere (green) and ocean/atmosphere (orange), and global warming levels their tipping points will likely be triggered at. Summary Map by Earth Commission/Globaïa.

GHG emissions are piling beyond critical limits leading to complex extreme events with more frequent climate risks and incidents along with unprecedented global temperature increase. These risks are escalating with every increment of global warming and their interactions with non-climatic risks are creating compound and cascading risks that are becoming more complex and difficult to manage. The year 2024 has been marked by record temperatures and extreme weather events. The UN is urging countries to drastically cut emissions and “move away from this path to ruin,” as additional climate action is needed more than ever.

Global average surface temperature from 1880-2020. (The Pennsylvania State University)

OperAI has been working with its strategic partners to help develop innovative and transformative tools to effectively harness synergies, streamline and speed up operational processes to adapt to and mitigate climate change within a sustainability perspective. Transformative tools that go beyond insights to actions and beyond status-quo to the implementation of these actions with the aim to help accelerate the transition towards sustainable net-zero economy.

OperAI has been built based on its long experience developing math-based innovative and practical methodologies to streamline and speed up operational processes. Some of these innovative methodologies developed with partners from academia and industry, involving machine learning that are currently used globally to develop crops to sustain heat and drought. Some of these crops sustaining heat and drought are currently grown in a number of countries

OperAI is based in Montreal and currently working with strategic partners developing new math-based tools involving ML, AI and Quantum technologies to help streamline and speed up climate actions.

OperAI has expertise addressing complexity that helped in speeding up processes in R&D, such the use of fractal geometry with neural networks to automate and speed up objects identification, including objects with irregular and subtle shapes. Some of this work was published in Fractals and Complexity in Nature international conferences by the World Scientific. Other work, which involves speeding up processes to adapt to and mitigate climate change was published by Taylor & Francis Group and at the Conference on Neural Information Processing Systems (NeurIPS),

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OperAI publishes also original articles and chapters (as above) as well as books spanning big data, machine learning, IoT, AI, QC to edge analytics.

Book on Edge Analytics by OperAI
“" "Both edge analytics and fog analytics involve pushing intelligence and processing capabilities down closer to where the data originates."
Automation World (2018)

Codes and examples of the edge analytics book above are available at the software development platform GitHub. These codes and examples include deep learning and reinforcement learning cases.

Image Explorer App

Signal App

Text App

Some of OperAI edge applications in the pipeline

The need to carry out real-time analytics and inference processes at the edges is driven by the unprecedented rapid proliferation of IoT devices along with the increase of machine-to-machine communication (M2M).

OperAI supports intelligent operations involving People, Processes, IoT, 5G, Quantum and M2M technologies to drive innovation, which in turn speeds up and streamlines operational processes within a sustainability perspective. OperAI uses a dual approach for a better outcome.

OperAI helps to carry out real-time analytics and streamline operational activities by leveraging Big Data, Machine Learning and Artificial Intelligence, Quantum Computing using different platforms.
OperAI focuses on operational data in the cloud and the edges to process and analyse data streams on real-time, whether as a signal, image or textual data.
OperAI works with partners in industry, research and academia in the realm of industry 4.0 for more accurate, efficient and environmentally friendly operational processes.

OperAI has expertise on preparing and developing AI/QC applications and tools that helped in speeding up discovery and delivery processes, with results considered as breakthroughs. These tools are used in research and development (R&D), including international research organisations and development agencies as well as industries.

Industry 4.0 refers to the current fourth industrial revolution where new technologies are all merging together, linking the real world to the digital world, involving big data, automation, ML and AI. It is named as the fourth generation in subsequent to the third industrial revolution on electronics and information technology to automate production. Industry 4.0 includes the Industrial Internet of things (IIoT), edge-to-cloud analytics, including fog analytics and cognitive analytics. The IIoT devices are poised, today, to increase production efficiency by optimizing and streamlining operations in the industrial equipment and machinery space. While the second industrial revolution refers to the use of electric power in the production system, the first refers to the mechanization of production using water and steam power.

OperAI co-develops IoT tools and AI applications involving cognitive analytics with people in the loop.

80%
of employees are keen to learn artificial intelligence skills and
are comfortable with AI in the workplace
(TechRepublic 2019).

80%
of Business and Tech Leaders mentioned that
AI creates jobs and increases productivity,
(BusinessWire, 2015).

Readers' feedback on the books

Excellent book! (Machine Learning)

"Excellent book, didactic, concise with a global view of the ML universe. I'm using it, with others, to prepare my introductory ML course. I also strongly recommend reading for executives who wish to have a comprehensive, well-written, concise overview of ML."

Fantastic book, Big Data of Things!

"Amazing book, goes from overview to details for most aspects of Big Data if not all, if you are a beginner to Data Sci, AI, Machine Learning, this book will help. Some content are mathematical, programming or scripting experience could enlighten you better. Overall time well spent, highly recommend it."

Services & Products

Reviews & Comments

Team Members

Operational AI to speed up processes of
production & protection at the edges,
with people in the loop.

OperAI co-develops also tools within the sustainability perspective
by including e-waste in the process of making new products with its
recycling partners.

IoT deployment can help to curb global warming by cutting carbon emissions by 15%.

OperAI has been selected among the first projects of the first NextAI Canada Montréal program, in 2018 as an edge AI-based analytics platform. It has been then launched officially with the support of the Government of Québec early 2019.

One of OperAI projects has been selected by Quantum Quebec and City of Montreal to be presented in 2022 - 2023 international events on emerging technologies in Japan, South Korea and Europe respectively. This project has been also hosted by CENTECH during 2022.

OperAI has joined the Evolution of Networked Services through a Corridor in Québec and Ontario for Research and Innovation program, known as ENCQOR, early 2020 and Réseau Montréal recently.
OperAI has also partners working on the development of IoTs in Québec.

OperAI is expanding in its partnership to include new partners to speed operational process and help to adapt to and mitigate climate change.

OperAI will be looking for members to join OperAI team. Looking for new members with math and/or engineering background and experience in programming and prototyping.

OperAI will be looking for developers to help with designing and developing applications in the cloud and the edges. The tasks include:
- Writing and compiling codes in R, Python, Spark, C++ and Q#.
- Debugging codes by testing and fixing errors, if any.
- Simulations and testing programs in real situations before their launch.
- Deployment and launch of applications, including mobile apps.

OperAI transforms operational processes for maximum efficiency while leading the rapid transition to a sustainable economy through innovative hybrid classic-quantum technologies.

We have been working to develop some of the tools than can help to address and speed-up some of the climate adaptation and mitigation challenges by proposing hybrid enhanced operational tools which leverage the power of classical or/and quantum emerging technologies.

Readers' feedback on the books

Excellent book! (Machine Learning)​"Excellent book, didactic, concise with a global view of the ML universe. I'm using it, with others, to prepare my introductory ML course. I also strongly recommend reading for executives who wish to have a comprehensive, well-written, concise overview of ML."

Excellent book! (Machine Learning)

"Excellent book, didactic, concise with a global view of the ML universe. I'm using it, with others, to prepare my introductory ML course. I also strongly recommend reading for executives who wish to have a comprehensive, well-written, concise overview of ML."