EAIGLE Is Turning Existing Security Cameras Into 30-Second Gate Transactions
EAIGLE founder and CEO Amir Hoss joined FreightWaves to break down how the company's computer vision platform is slashing gate dwell times and closing the yard's long-standing data gap. Fresh off a growth funding round and 350% year-over-year growth, Hoss detailed how EAIGLE turns cameras yards already have into a fully automated, paperless gate-to-dock operation. The post EAIGLE Is Turning…
EAIGLE has raised growth funding after experiencing 350% year-over-year growth, thanks to its AI platform that utilizes existing security cameras to reduce gate dwell times below 30 seconds, decrease detention, claims, and labor costs. Founder and CEO Amir Hoss explained how the company evolved from a specific customer problem and how the technology replaces ground-level gate operations.
EAIGLE's core lies in automation powered by computer vision, leveraging the cameras and infrastructure customers already have installed at the gate, yard, and dock to automate check-in, checkout, shipping, and receiving processes, resulting in fully unmanned, paperless, and real-time transactions. EAIGLE's solution targets the TLCC framework: time, labor, claims, and compliance.
By reducing gate transactions from seven and a half to 18 minutes to under 30 seconds, the company saves carriers and shippers hundreds of hours of driving time. The platform enhances security by validating bills of lading (BOL), purchase orders (PO), and paperwork in real time, preventing trailer theft and ensuring custody transfer.
It also addresses yard management systems' bottle neck by continuously updating the yard management system (YMS) with data from cameras on light poles, exterior walls, or mounted on shunt trucks. EAIGLE's infrastructure-agnostic approach allows it to work in inland facilities without heavy investments in camera installations, making the solution cost-effective for a wide range of facilities.
Written by urgent.news from FreightWaves's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.