> For the complete documentation index, see [llms.txt](https://presens.gitbook.io/wp/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://presens.gitbook.io/wp/applications.md). # Applications
Presens Network is a DePIN protocol delivering the spatiotemporal infrastructure for AI, robotics, and enterprises, transforming human presence in time and place into safer, smarter applications. *** ### **AI & Robotics Teams** Train models with real-world spatiotemporal context. Robotics and AI systems need to understand not just space but also when and where humans are present. Presens provides the presence layer for safe navigation, task scheduling, and predictive behaviors. *** ### **Urban Planners & Smart Cities** Visualize activity cycles for safer, smarter infrastructure. City planners gain an anonymous yet accurate view of foot traffic, activity patterns, and density flows, supporting better transit planning, zoning, and safety measures without invasive surveillance. *** ### **Logistics & Mobility** Optimize routes, fleets, and schedules in real time. From ride-hailing to last-mile delivery, mobility platforms use Presens signals to predict demand, avoid congestion, and improve routing efficiency. *** ### **Retail & Hospitality** Measure peak periods without tracking individuals. Businesses gain insights into local presence patterns, when people gather, how density shifts, enabling smarter staffing, promotions, and inventory planning without collecting personal data. *** ### **Research & Academia** A new open layer for human dynamics. Universities and research labs studying human mobility, epidemiology, or environmental impact can access anonymized presence data at global scale, accelerating open innovation.