Antes de Subir

Planning a safe mountaineering route usually causes analysis paralysis. Hikers have to jump between ten different sources—checking maps on one site, historical weather patterns on another, current trail hazards on social groups, and gear checklists on blogs. Antes de Subir solves this fragmentation by consolidating everything into a single, high-density interface.

The initial MVP focuses heavily on data quality. Instead of opening the platform to unreliable crowdsourced noise, every single trail data package is manually verified and structured. This ensures absolute precision across crucial metrics: map coordinates, dynamic climate data, objective difficulty scales, potential terrain risks, water requirements, and tailored gear checklists.

Architected entirely using Next.js and Tailwind CSS, the current iteration architecture leverages strict, type-safe local data structures to render content instantly. This setup allows for rapid layout iterations while the core data schema is being refined directly against real-world user feedback.

The long-term roadmap shifts towards automated infrastructure. The static data layer will transition into a relational cloud database, paving the way for an AI-powered ingestion engine designed to crawl regional data, synthesize trail conditions, and generate hyper-tailored route summaries and safety recommendations.