Overview
As more Public Sector (Federal, State, and Local) Transportation Agencies embrace the concept of Self-Screening Pods there is an opportunity to dramatically improve the passenger experience through the utilization of hyper-personalized digital signage or other forms of dynamic graphics. These digital dynamic graphics would be tailored to each person as they pass through the screening pods and informed by an AI that would curate applicable information to present to the passenger based on their pre-travel questionnaire.
Iternal's software would be able to deliver curated information to the screening pods (or other instances of digital signage) via API in any major media format (text, slides, audio, video, images) for maximum flexibility of use case and application. For example if a traveler does not speak English and only speaks French, the signage could receive information from Iternal's software in French to instruct the passenger to complete certain divestment tasks all in the French language.
You can use purchased credits across multiple use cases. ILIM Credits are the common unit for measuring platform system usage to include building stories, generating outputs, capturing IdeaBlocks®, saving stories, archiving stories, and downloading media and using various applications in the Iternal Technologies ecosystem. Credits purchased for use on AWS GovCloud FedRAMP High Infrastructure are not exchangeable with Commercial / Commercial Cloud Credits. Listing is for the software license only, does not include professional services or systems integration which will likely be required to realize the full potential of the system.
Highlights
- ~16x the speed at which your team is able to deliver the right information, to the right person, at the right time, in the right language.
- Public Sector Business and Digital Transformation through Automation
- Accurate and Always Up-to-date Information & More Effective Knowledge Sharing & Better Collaboration
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Public Transportation - Personalized Self-screening Signage GovCloud
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