5-Minute Chatbot Session With Keith Lehman

July 23rd, 2020

We’re fortunate enough to have the CTO and Co-Founder of Brightmerge, Keith Lehman, tell us more about how Brightmerge is a game-changer. With over 16 years working in AI and Machine Learning for companies such as Tokyo Electric Power, China Light and Power, Microsoft, and Bank of America, Keith brings in deep industry knowledge and a strong technical background to the team.

Can you give us the elevator pitch for Brightmerge?

Brightmerge is developing a SaaS platform that allows microgrid designers and project financiers to rapidly and confidently determine the optimal microgrid design for their site.  We are driving down the cost, time, and effort required to identify the optimal design.


What does economic optimization mean, and what are the benefits to the customer?

Economic optimization refers to maximizing benefits while minimizing the resources used to produce those benefits. Systems that utilize the principles of economic optimization are systems that provide the most value at the lowest cost. Usually, this means that the systems create less waste.  In the case of a microgrid, it means that the electricity users – a building or factory, for example – use more of the electrical energy to create value and loose less of the energy, or they use more of the energy from renewable resources than from polluting resources since the “fuel” cost for renewables is essentially 0.  


What are some advantages of bringing two companies based in different countries together to commercialize a product?

The modern economy is global, and innovation is occurring everywhere. The greatest advantage is that working across boarders forces us to adopt a global perspective from day one. We are both small, scrappy, startups – and yet we are working together to propose and win contracts in Africa, the US, Israel, and the E.U. We’re able to do this because, to meet the requirements for our respective local markets, we’ve had to build 

One example of how this perspective plays out is in the product requirements. The Israeli electrical market, for example, is much simpler than in the U.S. Still, since we are designing the product for both markets, we have had to build in the ability to model U.S. tariffs while the product is still in an early alpha stage. In a similar vein, due to Israel’s proximity to the E.U., Israeli developers are more conscious of the more stringent E.U. privacy laws, and design and build their products accordingly.


What is the product both companies are developing?

We are working together to create a Software as a Service (SaaS) solution that identifies the optimal architecture for microgrid solutions at our customer’s locations. We do this with an interface that makes it very easy to specify your business objectives and input the data that you have. Our A.I. does the rest – it constructs a model and, using the lessons learned from looking at tens of thousands of microgrids, efficiently identifies the most appropriate architectures. The A.I. then simulates variations of those architectures to predict both the operational and the financial performance you can expect them to achieve. Finally, the system provides the user with a report that describes the microgrid and includes all of the technical details that developers need to finalize their plans.

Microgrids are complex, and the regulatory, tariff, and incentive program rules are too.  They are too complicated for all but the best engineers to understand how everything works together, so many projects fail due to contradictory or competing goals and regulations or due to an overly simplified analysis of the plan. Since engineering is expensive, developers will take short cuts. One of the most common is to design based on a typical day, for example. We’ve built an expert in a box with enough data, memory, and computational strength to avoid the common pitfalls and hone in on the optimal plan. 


Can you describe each company’s unique expertise and the benefits each brings to the table?

Dynamic Grid is one of the leading A.I. companies with a really deep understanding of how to model complex decision making. We hear a lot these days about “big data.”  This typically means performing statistically analyzing large amounts of data to identify the patterns. Financial companies, for example, look at your purchase patterns and then ask questions like, “Does this particular purchase fit your purchasing patterns?” If the answer is no, the company flags the unusual transaction as possible fraud.  

We do this by modeling the larger system, and not simply the data patterns. By working with Dynamic Grid, we’re able to answer a whole different category of questions. While we are not in the financial services, it’s as if you could ask your credit card company, “Which purchase will work best for me?”

Brightmerge comes more from the industry application side of things. Our founders have been deeply involved in energy and energy management global product, application, and market development for decades, so we understand what the industry needs. We are intimately familiar with the data and processes that developers use when making their decisions. We have also been building applications and understand the demanding requirements that customers have when purchasing SaaS software. 

We’ve been working together now for a year and a half, and I have to say that as someone who has been running SaaS product development teams for close to 20 years (as long as SaaS has been around), I could not have asked for a better partner. Our two organizations complement each other exceptionally well, and I look forward to working with each Introspective for a long time. 


Who is the target customer, and what problem is the project going to solve?

The typical Brightmerge customer is an EPC or project development organization with annual revenues above $15,000,000/year. We’ve worked with organizations as small as a few million per year, as well as global corporations with revenues in the billions and managing assets in the hundreds of billions. We’re still early enough that we are working hard to identify our sweet spot. Smaller companies are nimble and hungry, so they adopt newer technologies more readily than larger companies. On the other hand, the larger companies have correspondingly larger portfolios, so they place a much greater value on our services. 

All of these companies have one thing in common: they understand the long-term value of using microgrid solutions to meet their customer’s electrical needs but know that consumers need to be confident that their designs are optimal, and their electricity costs are minimal.  With our software platform, they can accomplish those goals far more efficiently than by using conventional engineering approaches.