4.13.22-Build.nano-Startups-Lamarr.ai

SPEAKER 1: So good afternoon. My name is Norhan. I'm the co-founder and CEO at Lamarr.ai, and we are a software company where we're developing an autonomous solution for building envelope diagnosis using aerial data and cloud computing.

So we're living in an aging environment where every building being built add at least 50 years of carbon emission energy consumption and operational cost. And the US Department of Energy estimated that there would be around 10 million buildings that would be added to cities between 2016 and 2040 that would need to comply with the International Code Council. We also see the introduction of new building energy codes such as BERDO 2.0 and Local Law 97 that would require a mandatory building envelope audit to comply with these energy standards.

So while we talk about building specifically within the scope of our technology, we care about buildings, specifically aging buildings, where 50% of the building stock in the US were built before the year 1980. And also, 40% of the US emissions is coming from the building sector. But more specifically, we are interested in reducing energy losses from building envelope that could constitute to about 40% of energy saving.

So this required a growing interest in improving building audit process, which is currently a very costly and time-intensive process that starts with hiring multiple auditors to do site visits and very time-intensive equipment that would require sometimes the occupants of the building to leave the buildings. And that to report findings to key stakeholders. And this entire process takes around 60% of the time just to do the data collection that could be sometimes also inaccurate.

So in Lamarr we developed a solution that used aerial data, IR and RGB data, and artificial intelligence to detect anomalies in the building envelope. So you can look at our technology as giving your building an MRI scan, where we're not detecting tumors we're detecting building defects fully autonomously. So the core aspects of our patent technology is based on three key components, data collections from drones, and computer vision models to detect anomalies, and finally, generation of 3D models that could be integrated into building energy modeling.

So over the past four years this is a spin-off technology from a research collaboration between MIT, Georgia Tech, and Syracuse. They're funded by the Department of Energy. We have been developing the suitable flight path and data collection methods that are suitable for the building envelope audit.

And the data that we collect from drones are directly processed using proprietary computer vision an machine learning models that we have been developing over the years to detect building components, such as doors and windows, but most importantly, building thermal anomalies. So any defects in building envelope such as missing insulation or heat leakage. We detect this fully autonomously using our computer vision model. And then all these data that are processed from our algorithms are translated into a 3D model that carries the information of the anomaly location, the class of the anomaly, and directly integrated into 3D energy simulation models fully autonomously the current best practices and are able to produce.

So why our technology matters, through the pilots we have been conducting over the years we identified that our technology can cut down the time and building envelope ordered by 60%. Most importantly, we're also much cheaper. So we are 85% cheaper compared to the traditional building envelope audit. And we are safer, more accurate where we are eliminating the human aspects of data collection by replacing that with drones.

Currently, our key users are large property managers and individual home owners where they can directly upload their data set to our software, which I will show in the next slide to get results in a matter of seconds. So we have trained this model over thousands of images that we collected ourselves, where users can upload their data set get results instantly, and identify what kinds of repair are needed also. We have also done a large campus pilot with Georgia Tech, where we validated the performance of our algorithms and the results that we're getting with 20 building scientists also across MIT and Georgia Tech. And finally, our team has the right expertise to tackle this problem, where we have expertise from building technology, AI, best solution, urban analytics, and environmental solution.

Finally, before I conclude, I just want to say why we named our company Lamarr. It's a tribute to the actress and inventor Hedy Lamarr and all other inventors who didn't gain prominence during their time, especially women.

Finally, we're looking to partner with a large property management company, building retrofitting company, and building analytics to test and validate our technology. So if you're interested to learn more, we'll be in the next session. Thank you so much.

[APPLAUSE]

SPEAKER 2: Great job, Norhan. Thank you very much. And thank you to all of the presenters today. They'll be next door. I think it's now time for us to go to lunch. And then we'll be back for a great program this afternoon. Thank you, everybody, for a great morning.