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Automate the customer service experience for flight reservations using Amazon Lex

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As air travel starts to pick up in many parts of the world, digitization continues to transform the aviation industry. Airlines are working to reduce the number of touchpoints at the airport. Best practices have been implemented to minimize the number of physical interactions between employees and travelers. As a result, customer service is undergoing an accelerated transformation as airlines strive to provide a smooth and seamless experience. Airlines want to deliver a customer-centric experience that gives passengers a choice on how they engage to ensure high customer satisfaction.


Event-based fraud detection with direct customer calls using Amazon Connect

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Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that in some countries when fraud is suspected the credit card is blocked immediately, which leaves the cardholder without a reason as to why, how, or when. Depending on the situation, it can take anywhere from a few hours to days until the customer is notified and even longer to resolve. With Amazon Connect, a cardholder can be notified immediately of a suspected card fraud and interactively verify if the suspected transactions were indeed fraudulent over the phone.


Build an event-based tracking solution using Amazon Lookout for Vision

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Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Many enterprise customers want to identify missing components in products, damage to vehicles or structures, irregularities in production lines, minuscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision eliminates the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects--with no ML expertise required.


Building a scalable outbound call engine using Amazon Connect and Amazon Lex -- #ArtificialIntelligence #StartUp #iot #robotics #AI

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This is a guest post by AWS Machine Learning Hero Cyrus Wong. Staying connected with family, friends, and colleagues is easy for most people who live with or close to others. For educators who need to communicate lessons and schedules with their students, or businesses who communicate with new and existing customers, staying connected can be hard, especially in times of crisis and isolation. Specifically, I wanted to make remote communication between educators and students easier. Communicating time-sensitive information and confirming that students received messages can be hard; scaling communication from tens to thousands of students can make the problem more complex, impacting educator and student productivity, time, and overall experience.


Designing conversational experiences with sentiment analysis in Amazon Lex Amazon Web Services

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To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, "Sorry to hear you are having trouble." Understanding sentiment is also useful in determining when you need to hand over the call to a human agent for additional support. To achieve such a conversational flow with a bot, you have to detect the sentiment expressed by the user and react appropriately. Previously, you had to build a custom integration by using Comprehend APIs.