BagsID utilizes image-based tracking and computer vision technology to create a digital twin of each bag, enhancing baggage identification and profiling. This approach reduces mishandling rates and improves operational efficiency for airlines and airports, ultimately enhancing the passenger experience.
Funding
$5.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional baggage handling relies on physical tags and tracking technologies, which are prone to errors, leading to mishandling, increased costs for airlines and airports, and a less satisfactory passenger experience. Delays at the boarding gate are often caused by carry-on bag issues.
Solution
BagsID offers an AI-powered baggage recognition solution that uses image-based tracking to create a digital twin of each bag, identifying its unique characteristics such as size, color, material, and damage. The platform integrates with existing handling and IT systems to improve baggage management for both carry-on and checked baggage. By leveraging computer vision and data analytics, BagsID aims to reduce mishandling rates, improve operational efficiency, enhance security, and increase passenger satisfaction.
Target Audience
BagsID's primary customers include airport authorities, airlines, ground handlers, and security agencies seeking to modernize and automate their baggage handling processes.
Features
- **CarryOn:** Automatically counts, measures, and classifies cabin bags using computer vision and deep learning AI.
- **BagBridge:** Focuses on the identification of checked baggage, from tracking and sortation to damage assessment.
- Image-based digital twin creation for each bag based on visual characteristics.
- Integration with existing airport and airline IT infrastructure.