ProductsAug 26, 2026watch
Factory AI is a harder problem than a polished demo suggests. Lighting changes, objects move, processes vary, and mistakes have physical consequences. That is why a visual AI company aimed at the factory floor is worth tracking: it tests whether multimodal systems can become dependable operations software.
Why it matters: The risk is overpromising. Pagish will watch whether these systems work across messy deployments, not just controlled examples, and whether they integrate with the tools manufacturers already use to make decisions.
AI in PracticeAug 26, 2026watch
Medical AI becomes much more serious when it enters the operating room. A system that helps surgeons identify critical anatomy in real time is not a chatbot convenience; it is a decision-support layer inside a high-stakes procedure.
Why it matters: The standard has to be higher than novelty. Pagish will watch for peer-reviewed validation, regulatory pathways, surgeon accountability, and whether similar systems work across hospitals rather than in a single headline case.
AI in PracticeAug 24, 2026use-case watch
A research release applies vision models to road-safety auditing, emphasizing contexts where infrastructure data is scarce.
Why it matters: Useful AI adoption depends on practical deployments outside wealthy, data-rich environments.
ResearchAug 23, 2026watch
A recent arXiv paper introduces Inter-X++, a benchmark for multimodal human-human interaction analysis across perception and synthesis tasks.
Why it matters: Understanding human interaction is important for assistants, robotics, video models, and social AI systems. Better benchmarks help reveal where multimodal models still fail.