10.5.23-Showcase-Tokyo-Lamarr.AI

BENJAMIN ROCCI: Hi there. My name is Ben Rocci, and I lead Strategy and Business Development for Lamar AI where we are unlocking building energy efficiency through rapid thermal diagnostics.

When we talk about building energy efficiency, it's important that you understand the critical role that the building envelope plays. The building envelope means the exterior and roof of the building. By fixing defects in the envelope, as much as 40% of building energy losses can be eliminated. And moreover, envelope upgrades yield the highest ROI when compared to other energy efficiency spending options.

But unfortunately, identifying and diagnosing envelope defects is complex, expensive, and slow. That's because the way it's done today is called an energy audit, and it's highly manual. It involves site visits and interviews, intrusive expensive equipment, and manual report outs.

For a building of 10,000 square feet, for example, this process could take as long as 12 weeks and costs as much as $30,000. So this is where Lamar AI comes in. Lamar is like giving your building exterior an MRI scan to rapidly and cost effectively detect defects.

Where traditional methods take weeks, Lamar takes just days to provide actionable results. And through more than five pilot projects, we've demonstrated that our technology far outperforms traditional methods. We are 85% faster, 90% cheaper, and 50% more accurate than manual audit techniques.

So how does all of this work? Well, there's three key components. And the first involves thermal images captured by either drones or handheld cameras. Second, proprietary computer vision models that detect, diagnose, and classify anomalies. And third, 3D digital energy models that can be used to assess thermal performance and identify targeted retrofit strategies that maximize the return on your investment.

The product is a three-tiered subscription offering with the most comprehensive tier, Lamar Audit, consisting of anomaly localization and classification, anomaly registration on a 3D model, targeted retrofit recommendations, and a comprehensive 3D energy model.

No other company delivers this combination of highly automated processing and deep actionable insights as Lamar. And that's because this is the product of more than six years of research across MIT, Georgia Tech, and Syracuse. And it's what happens when you bring together leading building science experts with top AI expertise, a massive thermal anomaly data set, and proprietary AI algorithms.

So how do we work with customers? Well, customers come to us to solve problems like compliance and ESG reporting, retrofit planning, and ROI analysis, quality control on new construction, and pre-acquisition due diligence.

So here's an example of a recent project that we completed with a large property manager in Tampa, Florida. The customer came to us with a portfolio of 12 buildings and wanted to know how best to deploy retrofit spending.

We quickly scanned their portfolio of 12 sites and identified the worst performers and by choosing to work with Lamar over a traditional energy auditor, we helped the customer save more than 80 hours in engineering time, $15,000 in direct audit costs. We detected 50% more defects, and we helped the customer save over $300,000 in construction expenses as a result of higher accuracy data used for retrofit planning.

So this is our leadership team. Our leaders come from the top of their fields in building science, urban sustainability, and analytics, artificial intelligence, and architecture.

And quickly, I'd like to say why the company is named Lamar. This is meant to be a tribute to the actress and inventor Hedy Lamarr, who was the inventor of signal hopping technology, and a tribute to all inventors who did not gain prominence for their inventions during their time.

So please come and chat with me. We're looking for our first customers to run pilots in Japan and Korea. If you are a property owner, an ESG manager, you work with a construction firm, or you're a proptech investor I would love to chat. Thank you very much.

[APPLAUSE]