MarTech Interview with Jean-Simon Venne, CTO at BrainBox AI

MarTech Interview with Jean-Simon Venne, CTO at BrainBox AI

“Developing and running a sophisticated Machine Learning model and handling a number of data points in real-time requires immense computational power”

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How has your journey been through HVAC technology? What made you found BrainBox AI?

My journey into HVAC technology began while working on energy efficiency projects throughout North America and Europe. During this stage of my life, I dealt with technology in a plethora of buildings. These were buildings of different sizes and purpose, anything from hotels all the way to data centers. It quickly became apparent to me that continuous commissioning approaches would generate consistent energy savings but would require extensive amounts of both financial and human capital. With this in mind, I set out to find a way to engineer an AI base commissioning approach to deliver a new longer-lasting HVAC solution which would make efficient use of building tech to maximize savings while minimizing occupant discomfort. The final product of this journey was BrainBox AI, a solution that is both inexpensive and requires little human capital.

What are the challenges that BrainBox AI faces in integrating Artificial Intelligence into HVAC Technology?

The main challenge faced when integrating AI into HVAC Technology is to leverage existing building HVAC equipment and sensors to their highest capabilities. This is a highly relevant challenge for us as BrainBox AI focuses on utilizing the tools/sensors already in place within buildings for its data gathering and monitoring processes. For example, on occasion, certain sensors in the building, such as thermostats, might be incorrectly calibrated or not operational. Likewise, when HVAC equipment, such as boilers and ventilation systems, operates at reduced efficiency due to years of use, we must revise our control strategy in order to get the most out of them.

How does BrainBox AI platform work?

BrainBox AI is the first autonomous AI technology for HVAC; it is redefining building automation and is at the forefront of the green building revolution. BrainBox AI’s solution reduces operating costs and energy consumption by 25-35%, decreases a building’s carbon footprint by 20-40%, and improves occupant comfort by an average of 60%. Using advanced predictive control strategies with Machine Learning techniques, BrainBox AI has developed an Artificial Intelligence approach which tailors itself to each individual building’s specific needs. More specifically, BrainBox AI’s Artificial Intelligence engine, together with our proprietary process, allows a building to move from reactive to preemptive operations management in three steps:

STEP 1: Our solution identifies and catalogs your building’s specific operating behavior and energy flow by gathering data from both internal and external sources. It then creates a building energy profile for making informed predictions about future energy flow. BrainBox AI collects hundreds of thousands of real-time data points, such as outside temperature, sun/cloud positioning, fan speed, duct pressure, heater status, humidity levels, and occupant density, among others.

STEP 2: Our AI engine instructs your existing HVAC system on how to operate more intelligently and efficiently. This process is similar to an aircraft on auto-pilot.

STEP 3: Our solution continually amalgamates and analyzes all generated data to further optimize operational efficiency and discover other unique insights.

How can your platform benefit modern structures and the companies that build them?

Optimizing a building’s current HVAC system fulfills every building owner’s prime objective: maximizing energy savings and reducing operating costs. Additionally, BrainBox AI monitors the heartbeat of every piece of installed HVAC equipment, ensuring each is performing properly and continuously commissioned, consequently reducing the number of service calls made. This platform can easily be integrated into any building, regardless of its age, improving both the overall comfort level and energy efficiency of a modern structure with minimal effort required from the building’s owner.

How does Deep Learning and Cloud-based computing bolster your platform?

Deep Learning allows BrainBox AI to conduct a thorough analysis of extracted raw data features in order to make highly accurate multi-layered predictions concerning the future behavior of a given building section. The implementation of Deep Learning allows the decisions BrainBox AI makes to accurately counteract numerous unfavorable building conditions, such as increased sunlight exposure or excessive heat generation from building occupants entering a room, among others. In so doing, the BrainBox AI solution can preemptively begin to reduce building temperature, maximizing occupant comfort and generating considerable energy savings. Developing and running a sophisticated Machine Learning model handling hundreds or thousands of data points extracted from a structure in real-time requires immense amounts of computational power. Cloud-based computing acts as a solution for this as it allows for the AI software to make thousands of micro-adjustments to a myriad of individual zones within buildings with ease.

Does your platform eliminate the need for human HVAC maintenance engineers/operators?

Not entirely. While BrainBox AI does greatly reduce the responsibilities of HVAC maintenance engineers and operators, there are certain tasks which a computer-based solution cannot accomplish. For example, while such personnel would no longer be required to manually adjust HVAC parameters and temperature settings in building sections, such as room temperature setpoints or fan speeds, they would still need to be on-site to repair or replace the equipment, and would also retain the task of performing routine HVAC device inspections.

How has BrainBox AI’s platform benefitted a business?

In 2018, BrainBox AI was installed in two company-owned retail stores in the Greater Montreal area. This company needed a low CapEx solution to optimize the HVAC system in both stores, as each facility has different operating hours, are located in a different urban environment and experience very unique occupancy levels throughout the day. Following the BrainBox AI algorithm deployment, these commercial venues saw overall energy savings of 28% and 31% respectively.

Which events and webinars would you suggest to our readers as being the best in grasping information on emerging technologies?

Personally, in terms of emerging technologies, I would strongly recommend IVADO (The Institute for Data Valorization: A Scientific and Economic Data Science Hub) events, MIT Blogs and discussion posts as well as World Summit AI Conferences.

What is your take on the weaponization of AI?

It should absolutely not be done. AI is extremely efficient at coming to logical conclusions and making the correct logical choices. However, AI is incapable of understanding and acting on moral values. This flaw can be extremely dangerous in situations where human understanding is key. Weaponizing such technology can most certainly have unsafe, unpredictable consequences.

How do you see the future of AI?

AI will essentially begin to disrupt all known markets. The near future will see AI dominating all sorts of new fields. Take for example reducing car use and traffic with autonomous ride sharing. Numerous world problems such as global warming and energy management need efficient solutions and AI can be an invaluable tool in solving them.

What start-ups are you keenly following?

I am really fascinated by the work being done at Element AI as well as at Mnubo.

Which specific spheres in AI are you particularly interested in?

Spheres that are of particular interest include advanced Neural Networking as well as Deep Learning AI software. Both are imperative to the overall growth of AI and it will be interesting to see how such technology progresses in the coming years.

Tag the one person in the industry whose answers to these questions you would love to read.

Yann LeCun

Thank you, Jean-Simon! That was fun and hope to see you back on MarTech Series soon.

Jean-Simon Venne is a co-founder and CTO of BrainBox AI. As a technology expert specializing in the fast and efficient migration of technological innovations to commercial applications, Jean-Simon has over 25 years of experience developing and implementing new technology to solve long-standing commercial issues in the fields of telecommunications, biotechnology, and energy-efficiency.

Prior to joining BrainBox AI, he was responsible for the successful integration of M2M technology in over 200 Smart Buildings across North America, Europe, and the Middle East. Jean-Simon holds a B.Eng. in Industrial Engineering from École Polytechnique de Montréal and a Certificate in Logistics from the University of Georgia Tech.


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BrainBox AI uses deep learning, cloud-based computing, algorithms and a proprietary process to support a 24/7 self-operating building that requires no human intervention and enables maximum energy efficiency. Pre-commercialization tests have demonstrated that BrainBox AI enables a 25-35% reduction in total energy costs in less than three months, with low to no CAPEX needed from property owners. It also improves occupant comfort by 60% and decreases the carbon footprint of a building by 20-40%.

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The MTS Martech Interview Series is a fun Q&A style chat which we really enjoy doing with martech leaders. With inspiration from Lifehacker’s How I work interviews, the MarTech Series Interviews follows a two part format On Marketing Technology, and This Is How I Work. The format was chosen because when we decided to start an interview series with the biggest and brightest minds in martech – we wanted to get insight into two areas … one – their ideas on marketing tech and two – insights into the philosophy and methods that make these leaders tick.