gtechna
gtechna’s smart parking management and enforcement solution is trusted by leading cities worldwide. Our cloud-based system empowers municipalities, transportation agencies, and universities with cutting-edge applications that increase parking revenue, cut operational costs, and improve the driver experience.
By choosing gtechna for your parking management and enforcement needs, you're partnering with an industry leader known for constant innovation. Cities like Washington, D.C., Boston, Pittsburgh, Toronto, and Vancouver have already transformed their parking infrastructure with gtechna—now it's your turn to experience the future of parking.
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Vehicle Acquisition Network (VAN)
Vehicle Acquisition Network (VAN) is an automotive software platform built to help car dealerships acquire high-quality used vehicles directly from private sellers—without relying on auctions. As wholesale prices rise and vehicle availability tightens, VAN empowers dealers with tools to source inventory faster, more profitably, and with greater control.
VAN aggregates local FSBO (for-sale-by-owner) listings, applies real-time market data to assess profitability, and automates communication with sellers at scale. Buyers can manage leads, track seller conversations, and streamline acquisition workflows through an intuitive CRM-style dashboard designed specifically for dealership teams.
For dealers who don’t have dedicated acquisition staff, VAN offers a Managed Buyer program, pairing stores with expert buyers who actively source, engage, and negotiate with private sellers on their behalf—saving time and boosting acquisition volume without internal hiring.
VAN is trusted by hundreds of dealerships across North America—from independent rooftops to franchise groups—looking to beat Carvana and CarMax at their own game. It's the smarter way to buy cars.
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Carmen Axis Video Application
The Carmen® Axis Video Application significantly upgrades Axis cameras by integrating sophisticated Automatic Number Plate Recognition (ANPR) and Make and Model Recognition (MMR) features. Users can choose from various deployment methods, including cloud processing through Carmen® Cloud for enhanced scalability and worldwide accessibility, or opt for on-camera processing that guarantees secure and dependable functionality without the need for extra equipment. This application delivers outstanding precision in recognizing license plates across the globe, enabling real-time data collection on vehicles for purposes such as traffic management, security measures, and access control systems. With its ability to seamlessly integrate and offer adaptable licensing arrangements, it serves as a highly effective solution tailored to meet a range of operational requirements. Additionally, the application’s user-friendly interface facilitates ease of use for operators in diverse environments.
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Carmen Worker
Carmen® Worker is an on-site Automatic Number Plate Recognition (ANPR) software that provides rapid, real-time vehicle identification tailored to your organization's infrastructure. Built to function without reliance on cloud platforms, it guarantees total data confidentiality and minimal processing delays, making it well-suited for sectors that demand stringent data governance, including access control, parking oversight, toll collection, and traffic regulation. This software can recognize license plates from over 160 countries and accommodates more than 38,000 different plate designs, enabling seamless integration with current systems via its Vehicle API and Transport API for straightforward implementation across diverse infrastructure configurations. Moreover, Carmen® Worker is fully adaptable, offering customizable options to satisfy the specific requirements of each deployment, which includes regional settings and parameter tweaks that enhance precision and effectiveness in various operational contexts. Additionally, the software's flexibility makes it a preferred choice for organizations looking to improve operational workflows while maintaining high standards of data security.
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