Meet the Founder
Stephen Southin is the Founder and CEO of Halo Eye and the Founder and Board Member of PAVE.ai. He built and scaled AI vehicle inspections to over 1.5 million per year across North America and Europe. Today he leads Halo Eye in building the universal capture interface for computer vision, making visual data consistent, verifiable, and ready for enterprise-scale deployment.
About Stephen
Stephen Southin is a Canadian entrepreneur who has spent his career building AI-native products for the automotive, fleet, and computer vision industries. As the founder of PAVE.ai, he created one of the first mobile inspection platforms to deliver automated visual assessments for enterprise fleets and marketplaces. Now at Halo Eye, Stephen is applying that same discipline and product focus to solve the hardest part of computer vision: data reliability. His work centers on designing systems that verify every pixel, ensure consistent capture across devices, and make visual data trusted by AI and humans alike. Stephen’s background spans AI productization, visual quality assurance, enterprise SaaS, and large-scale data operations. He continues to bridge applied AI research and real-world deployment through partnerships with leading enterprises and research programs such as the Vector Institute’s FastLane.
Building for what comes next
Stephen believes the next decade of AI will be defined by reliability and trust. The future belongs to systems that can prove what they see. With Halo Eye, his mission is to make capture measurable, explainable, and fraud-resistant at scale.
Every design decision follows a simple rule: AI is only as strong as the data that teaches it.
Halo Eye’s capture technology standardizes image quality, validates metadata, and ensures every session meets enterprise requirements before upload.
How Stephen empowers enterprises
Stephen approaches AI productization with an enterprise-first mindset. His leadership combines innovation with operational discipline to ensure AI systems perform under real-world conditions.
He works with fleets, insurers, and marketplaces to bring visual automation into production environments that require uptime, traceability, and accuracy. His approach bridges the gap between applied research and the business outcomes that define success.