Keith Gipson


One of the top building automation technicians at Honeywell in the late 80's and engineers at Johnson Controls in the mid 90's, Keith Gipson has been a pioneer in the Buildings IoT, Enterprise Energy Management (EEM) industries and technology for over twenty-five years.

In 1997 Keith Co-Founded the first EEM company, Silicon Energy Corp., which was sold to Itron in 2001 for $71 M. In 2005 Keith co-founded Shield Ops Inc. He architected an AI driven CSI and evidence linking program to help law enforcement discover, classify and correlate crime data as featured on the television show CSI Las Vegas (Season 10, Episode 4 "Coup De Grace").

Most recently Keith was CTO of Phoenix Energy Technologies where he architected the Enterprise DX (“Data eXchange") EEM platform that monitors and controls over 16,000 retail and commercial buildings, one of the largest integrated, web-based EEM platforms ever created. In March of 2020, after thirteen years, Keith left Phoenix ET to apply his vision and experience to build a conversational AI Autonomous Optimization (AIAO) solution. Keith holds a United States patent (US 6,178,362 Enterprise Energy Management System and Method 2001) and was honored by Southern California Edison as “A Modern Day African-American Inventor” for his contributions to the electric utility industry. He resides in Los Angeles, CA with his wife Andrea.

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Pete Willette

President / COO

Pete has 11 years of direct experience in the energy industry. He has succeeded in environments ranging from Fortune 50 companies to small startups with only five employees. He is a technically proficient engineer with astute business acumen and a strong background in data and analytics. These interests have driven him to start several small businesses, one of which won a client $5 M in grant funding from the California state government.

Pete spent time at Phoenix Energy Technologies as a data analyst. During this tenure, he learned about and realized the power of a data and analytic driven machine. Recognizing that anyone can use data analytics to ask what is, instead of hypothesizing and testing (the scientific method), has changed his problem-solving approach. Applying these methods, he has helped organizations from universities to large corporations make better-informed decisions than times past.

Most recently, Pete has become heavily involved in business development and go to market strategies. His primary focus is on gaining high-impact funding for companies via grants, venture capital, and or angel investment. He is familiar with the entire value stream relating to fundraising, finance, and legal ramifications. He also spearheads product-market fit campaigns, where a company can understand if they have market fit before having a prototype.

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Paul Campbell

Vice President of AI & ML Solutions

Paul is an experienced energy data strategist and has been an enterprise energy decision management, data velocity and applied analytics practitioner throughout his near 30 year career.

In 2019 at the Association of Energy Engineers East conference in Boston, Paul co-presented Virtual Closed Loop Control for Adaptive Energy Management in Retail Buildings with Keith Gipson. This presentation was based on a whitepaper that Paul wrote to contextualize the relative imprecision of Building Automation Systems based on an Open Loop design and that control systems based on simplified PID Loops are inadequate to achieve the precision of Closed Loop Control. Without closing the loop with a full expression of error correction, precision can never be achieved.

In 2021, Paul will be presenting on the powerful influence of Object Oriented Design in Energy & Decision Management where he establishes that one cannot manage energy without also managing decisions. This understanding is foundational to developing a fully automated Decision Management system to handle analytics signal traffic.

Paul’s ultimate objective is a multi-planar suite of robust programmatic and dynamic energy and decision management methodologies that responds continually to mechanical, environmental and climatological conditions seamlessly through machine learning and artificial intelligence techniques.

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