Problem: 

In dense urban areas like New York City, San Francisco, Chicago, Boston, and Atlanta, GPS often falters due to the “urban canyon” effect. High-rise buildings disrupt and block satellite signals, causing navigation systems to perform poorly, leading to imprecise positioning, incorrect pick-up/drop-off points, and confusion for services that rely heavily on precise location data. Even leading mapping platforms like Google Maps and Apple Maps face significant challenges in these complex cityscapes.

For autonomous vehicle developers and tech companies focused on reliable, last-mile delivery, this limitation becomes critical. Vehicles must often rely on visual and inertial cues when GPS data degrades, and even a slight misstep in location accuracy can create logistical problems. In a landscape where missed deliveries or misplaced ride-hailing pickups can directly impact customer satisfaction, there is an undeniable need for a more resilient solution.

Skyline Nav AI offers a proven alternative that bypasses GPS limitations. Our flagship product, Skyline Match AI™, utilizes advanced computer vision and precomputed 2D and 3D datasets to detect skylines, terrain features, and city landmarks at the pixel level. This approach enables real-time navigation with unparalleled accuracy—locating within 5 meters, 95% of the time, even in the most GPS-challenged environments, without relying on cellular or Wi-Fi connectivity​​. Sub 1-meter results are often possible by fusing the results with other sensors, like inertial and LiDAR on mobile devices and autonomous vehicles.