Most edge vision deployments fail not because of model architecture, but because real-world training data is structurally incomplete. Sampled data can’t cover combinatorial edge cases, forcing perpetual retraining cycles that break embedded deployment, explainability requirements and silicon viability. In this session, we will explore what changes when training data is complete by design rather than sampled by accident. We will present peer-reviewed results showing synthetic approaches outperforming real-world data by 34%. We’ll explain how physics-based synthetic generation provides deterministic control over geometry, lighting, occlusion, materials and sensors, and discuss implications for deployment on processors, ASICs and FPGAs. We’ll also introduce a new class of models that work on first deployment and explain how complete training data enables regulatory compliance.
Why Edge Vision Models Keep Breaking—and What Complete Training Data Changes
Session Speakers
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David Scott
Founder and CEO, Synetic
David Scott is a seasoned entrepreneur and technologist with a proven track record of building innovative platforms at the intersection of artificial intelligence, automation and real-world systems. He is the Founder and CEO of Synetic. Prior to Synetic, David founded and built The Main Branch, a technology services firm delivering custom software, automation and product development for forward-thinking clients across multiple industries. Through this work, The Main Branch became a trusted partner for domain-specific technical solutions, and it was here that David identified a recurring bottleneck in AI systems: the lack of high-fidelity training data. That insight ultimately led to the creation of Synetic. David brings a hands-on technical approach, blending deep software engineering expertise with a strong product mindset. He excels at translating emerging technologies into practical, business-critical tools and continues to push the boundaries of AI-driven perception systems that move beyond seeing to true understanding.
