SYNETIC.ai
Our Mission
Computer vision has a data problem. Collecting real-world data is slow, expensive, and filled with errors. Manual labeling costs and error rates can cause companies to easily outpace ROI with cost. Even if it doesn’t outpace ROI, companies can spend months gathering data, only to discover critical edge cases are missing.
Synetic AI solves this. We generate perfect synthetic training data with zero annotation errors, complete edge case coverage, and multi-modal sensor support all delivered in weeks, not months.
Our mission is to make state-of-the-art computer vision accessible to every company, regardless of their ML expertise or data collection capabilities.
Leadership Team
Experts in computer vision, synthetic data generation, and machine learning engineering.
David Scott
CEO & Co-Founder
David founded Synetic AI after 25 years in software engineering, including 15 years running The Main Branch consulting company. While building computer vision systems for defense contractors and Fortune 500 manufacturers, he identified a critical bottleneck: training data was too expensive, too slow, and limited by real-world constraints. He built Synetic AI’s procedural rendering platform to solve this—independently validated by USC researchers to outperform real-world data by 34%.
Trevor Satterfield
Chief Creative Officer
Trevor brings 17 years of startup and creative leadership, having guided multiple products to acquisition including early work at TestFlight (acquired by Apple). He’s led design teams creating VR assets and 3D models for Fortune 500 clients, bringing that expertise to ensure Synetic’s synthetic data meets production-grade visual fidelity standards.
Pax
Head of AI
Pax holds an M.S. in Computational Perception and Robotics from Georgia Tech and a B.S. in Aeronautical and Astronautical Engineering from the University of Washington. With extensive defense AI experience, he leads Synetic AI’s technical development, overseeing model architecture and training pipelines to ensure measurable performance improvements in challenging real-world environments.
Dana Walsh
Head of Sales
Dana has 15+ years of enterprise software sales, including 5+ years specializing in AI training data at a $1B+ company where she managed strategic accounts with top-tier technology leaders. Her consultative approach ensures customers get solutions tailored to their specific use cases, from initial data generation through full-scale deployment.
Will Ruffalo
Chief Strategy Officer
Will brings 10 years of corporate development, M&A, and strategic finance experience across startups, Fortune 500s, and investment funds. He’s deployed agricultural technology internationally, including several years in Africa, giving him practical insight into real-world implementation constraints. At Synetic AI, he leads partnership development and go-to-market strategy.
Tricia Baran
CFO
Tricia brings 30 years of accounting, audit, and finance experience, including her work as an auditor at Deloitte serving enterprise clients. Her Big 4 background ensures Synetic AI operates with the financial controls and compliance standards expected by defense and Fortune 500 customers, supporting long-term partnerships.
Our Technology
Synetic AI uses physics-based rendering and advanced simulation to generate photorealistic synthetic training data. Unlike traditional synthetic data providers, our approach produces data that doesn’t just match real-world performance—it exceeds it.
Physics-Based Rendering
Accurate light transport, material properties, and sensor simulation produce training data that eliminates the domain gap entirely.
Multi-Modal Generation
RGB, depth, thermal, LiDAR, and radar—all perfectly aligned with zero calibration errors.
Perfect Annotations
100% accurate labels with pixel-perfect segmentation masks, 3D bounding boxes, and complete metadata.
Edge Case Coverage
Generate rare scenarios, difficult lighting conditions, and edge cases that are impossible to collect in the real world.
The result: models trained on our synthetic data outperform those trained on real-world data by an average of 34%, as validated by independent university research.
University Partnership
We partnered with the University of South Carolina to validate our approach through rigorous, peer-reviewed research. Researchers trained seven different model architectures exclusively on Synetic synthetic data and tested them on real-world validation sets. The results were clear and consistent.
34%
Average mAP improvement over real-world trained models
7
Model architectures tested (YOLO v5/v8/v11/v12, RT-DETR)
100%
Validated on real-world test images
“The Synetic-generated dataset provided a remarkably clean and robust training signal. Our analysis confirmed the superior feature diversity of the synthetic data.”
— Dr. Ramtin Zand & James Blake Seekings, University of South Carolina
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