Special Episode: Skema.ai's Origin Story with Co-Founders Marty Rozmanith + Charlie Cichetti

💚 The Green Impact Report Quick take: Marty Rozmanith spent seven years writing the requirements for Revit — the tool that runs $1.2B a year in the AEC industry — and now he's building what comes after it. His pitch: pair AI with discrete engineering solvers so every design choice is explainable, not just fast, and use that to strip the padding owners quietly add to every project "just in case." 🤝 Meet Your Fellow Sustainability Champion Charlie Cichetti Co-Founder & CEO of Skema.ai, serial entrepreneur, LEED Fellow, and longtime sustainability advocate. Charlie has spent his career helping building owners operate smarter, greener, and more efficiently. Today, he's focused on building a tech startup using AI to dramatically reduce waste, inefficiency, and rework across the building lifecycle. Marty Rozmanith Co-Founder & CTO of Skema.ai. He started his career as a carpenter on a framing crew before earning an engineering degree and working in architecture and structural engineering. He later joined Autodesk and was recruited onto the original Revit team, where he ran the Revit product requirements group for its first seven years - shaping features still core to the product today. After Revit, he worked at Dassault Systèmes, the French engineering-software giant behind the design tools used across multiple industries, before co-founding Skema.ai in 2022 with Charlie Cichetti (and others) to bring that same rigor to buildings. 🌱 Breaking Ground on Better Building In this episode, Marty Rozmanith revolutionizes traditional construction approaches: ✍️ Key Insight #1: Pairing AI with discrete solvers so every design decision has a documented reason. The Challenge: AI alone is a black box — ask it the same design question ten times and it can produce ten different answers, which is a non-starter when a licensed engineer has to sign off and explain a decision. The Solution: Skema.ai combines traditional discrete-math solvers (which handle the hard engineering constraints — cooling, power, layout) with AI that learns to navigate hundreds of interdependent variables and explain why it made each trade-off. ROI: Owners get repeatable, defensible design output — like a data center configuration that names the exact constraint behind every chiller count — without sacrificing the speed AI promises. 🧠 Key Insight #2: "Morphing" — reusing what architects already built instead of starting from a blank page. The Challenge: Most cloud design tools ask architects to rebui