Looq AI

Where Looq earns its keep is the sites drones can't fly and LiDAR rigs are overkill: substations, congested distribution corridors, urban blocks near airspace restrictions, and pole lines that need engineering-grade models rather than photos in a folder. A crew walks, shoots, and the cloud returns georeferenced 3D and CAD linework. That's a real reduction in field hours for survey and T&D teams, and PPK positioning on the capture hardware is what makes the output defensible in an engineering review. qPole is the piece I'd trial first. Converting pole photographs into 3D models attacks a task utilities still do by hand, and the recent qPole webinar and the DTECH Power Player nod both point at power-delivery work as the company's center of gravity. If you're a T&D engineer with thousands of aging assets and thin inspection records, that combination — capture plus pole model plus condition data — is the reason to look. When to pick this: you're doing topographic, boundary, ALTA, as-built, or pole/route documentation on sites you can physically walk, and you need survey-grade output without booking a flight window or buying a mobile LiDAR rig. Telecom teams chasing BEAD documentation should also read the verifiable-records pitch closely, since funding disputes hinge on provable as-built data. When to pass: anything that needs the sky. Roof-level detail, canopy-penetrated ground returns, multi-mile corridor mapping where a vehicle or drone is cheaper per acre, or specs that demand mobile-LiDAR point density. This is ground-level photogrammetry and the limits follow from that. Also plan around the cloud: processing lives in Looq's environment, so fully offline field-to-deliverable workflows aren't the model here. Closest alternative is drone photogrammetry with ground