With the core geolocation engine stable and field-tested, I shifted focus to planning the final week before investor outreach. The remaining schedule covers final validation testing, demo video production, and investor outreach preparation.
The development sprint that brought me here — twelve days of building, testing, breaking, and fixing — produced a system that goes from receiving a raw radio signal to placing a pin on a map showing where that signal came from, in real time, while driving. It's not perfect. The accuracy varies with terrain, distance, and the type of radio being detected. But it works, it works reliably enough to demonstrate on camera, and the engineering path to improving it is clear.
The platform now spans three environments and thousands of lines of code across Python, Kotlin, and shell scripts. It handles terrain-aware RF propagation modeling, real-time signal processing with narrowband filtering and DC spike correction, parallel Bayesian grid search for geolocation, automatic jurisdiction detection and frequency switching, a comprehensive talkgroup identification database covering hundreds of agencies, Android Auto integration for in-vehicle display, and a road network constraint system using OpenStreetMap data. Each of these capabilities was built, tested, broken in the field, and rebuilt — often more than once.
The lesson that keeps reinforcing itself: the gap between working-in-theory and working-in-the-field is where the real engineering happens. Every field test surfaced problems that bench testing couldn't predict — impossible threshold values, deduplication keys that didn't match, hardware interactions between components that worked perfectly in isolation. The only way through is to drive, analyze, hypothesize, fix, and drive again.