Bridging Research and Revenue in Deep Tech

Deep tech companies follow a predictable trajectory: Phase 1: Technical breakthrough. A team of brilliant engineers or scientists builds something genuinely innovative. Maybe it's a new sensor technology. Maybe it's an AI model that actually works. Maybe it's hardware that nobody else can manufacture. Phase 2: Grant funding and pilot projects. The technology gets validated. Technical papers get published. Pilot customers confirm the capability. Everything looks promising. Phase 3: The gap appears. It's time to scale commercially. And suddenly, nothing works. The sales team can't explain the product. Marketing can't differentiate it. Customer success can't support it. The technology that was so clear in the lab becomes incomprehensible in the market. I saw this at Mireo, where we had genuinely advanced fleet management technology—tracking systems, telematics, real-time positioning—that engineers understood perfectly and customers couldn't grasp at all.

Technical founders often make the same mistake: they assume the technology sells itself. It doesn't. It never does. The curse of knowledge hits hard in deep tech. When you've spent years developing something, its value seems obvious. But customers don't have your context. They don't care about the technical elegance. They care about their problems. I've watched brilliant founders give demos that leave customers more confused than excited. They explain how the technology works instead of what it does for the customer. They use precision when they need clarity. The gap isn't intelligence—these are often the smartest people I've worked with. The gap is perspective. They're so close to the technology that they can't see it from outside. What's needed is translation: someone who understands the technology deeply enough to explain it simply, and understands the market deeply enough to know which parts matter.

Pure software companies can iterate fast. Ship something broken, fix it next sprint. The feedback loop is tight. Deep tech—especially anything with hardware—doesn't work that way. At NABR, I shipped three 0-to-1 products that combined hardware and software: Virtual 3D Tour (camera systems plus visualization software), Capital Planner (sensor data plus financial modeling), and a SalesOps Platform (physical showrooms plus digital tools). Each one taught me the same lesson: hardware constraints change everything. You can't iterate on hardware weekly. Manufacturing has lead times. Physical installations have dependencies. Customer deployments take months, not days. The feedback loop that makes SaaS forgiving doesn't exist. This means product decisions have to be right earlier. You can't afford to discover product-market fit through rapid iteration—by the time you realize you're wrong, you've already committed to manufacturing. The solution isn't to avoid hardware. It's to be rigorous about customer discovery before committing to build. Talk to ten times more customers than you think you need. Validate assumptions with pilots before scaling. And design your software layer to be flexible enough to compensate for hardware's rigidity.

Translating deep tech for markets involves specific work: Feature to outcome mapping. Every technical capability needs a customer outcome attached. "Real-time GPS positioning with 2-meter accuracy" becomes "Always know where your fleet is—no more lost vehicles or missed deliveries." The first is technically accurate; the second is commercially useful. Complexity hiding. Most customers don't need to understand how your technology works. They need to trust that it works and know what it delivers. At Pangea.ai, we had sophisticated AI matching algorithms. Customers didn't need to understand the models—they needed to understand that they'd get better candidates faster. Use case prioritization. Deep tech often enables many possible applications. Product translation means picking which ones to focus on first—usually the ones where the value is clearest and the sales cycle is shortest. You can expand later; you need revenue now. Technical-to-commercial handoffs. Sales teams need materials they can actually use: demo scripts that don't require engineering degrees, competitive positioning that makes sense to customers, and pricing that maps to value delivered rather than complexity built.

Product operators in deep tech need a specific set of capabilities: Technical fluency without technical depth. You don't need to write the algorithms, but you need to understand their constraints. You need to know which claims are defensible and which are marketing hype. Without this, you can't translate accurately. Commercial pragmatism. The market doesn't care about your technology roadmap. It cares about its problems. Product operators need to be willing to de-prioritize technically impressive features for commercially important ones. Bridge-building. In my experience, the hardest part is getting technical teams and commercial teams to respect each other. Engineers often dismiss sales concerns as "not understanding the tech." Sales often dismisses engineering concerns as "perfectionism." Someone needs to sit in the middle and translate both directions. At Mireo, we grew revenue from €600K to €2.1M ARR by restructuring the business model around customer retention—not by adding technical features. The technology was already excellent. What was missing was the commercial architecture to capture value from it. Sometimes the most important product work isn't product at all.