Let’s assume there is a government agency that requires its citizens to submit pension/retirement benefit claims using a customer-facing web/mobile application. Here’s a fictional use case to showcase the value of our solution. These microservices showcase the ease of integrating both native AWS services and external API-dependent architectures. The solution also provides model microservices for weather forecasting, pizza ordering, and appointment scheduling. We call this core process Intent Resolution Engine. This determines the capable bot microservice that can answer the customer’s question. Every interaction comes via Amazon API Gateway and gets synthesized by a core AWS Lambda function. The solution also provides a sample web application hosted on Amazon Simple Storage Service (S3) and accessed by Amazon CloudFront. This new integration supports multiple languages in a single bot, the addition of new language to an existing bot, and other features. Since the latest release of this Framework (v1.5.0), the Solution has integrated Amazon Lex V2 as an option for the brain module of the chatbot. The Serverless Bot Framework solution uses AWS CloudFormation to automate the deployment of framework architecture into AWS Cloud, as shown in Figure 1.įigure 1. In addition, it alleviates the need to engage a CSR. This chatbot can be customized to enrich customer support with intelligent, self-service, and life-like experiences. It can also be extended to expand the scope of customer support by integrating with the existing Salesforce ecosystem. This is an AWS Solutions Implementation that can be used to add sophisticated conversational chatbots to an existing web or mobile application. In this blog post, we’ll explore a pre-packaged AWS architect-vetted solution called the Serverless Bot Framework. Chatbots can help businesses save annual support costs by over 50% and reduce customer issue resolution wait-times by up to 90%. Among these are 24/7 customer support with no agent wait-times, improved operational excellence, self-learning intelligence, efficient use of Customer Service Representative (CSR) agents, and more. An intelligent chatbot on top of customer-facing platforms comes with inherent benefits. Businesses realize these interactions are resulting in quicker resolutions of customer concerns than a more traditional approach of agent interactions. Thus, a student support chatbot is recommended for any HEI to minimize the time spent repeatedly answering questions, and this helps the staff, advisors, and faculties utilize the time on other academic performance.Conversational interfaces have become increasingly popular, both on web and mobile. The cognitive services include different languages for text-based, voice-based, and graphical user interface requests with sensitive as well as non-sensitive requests. Most students individually ask the same question repeatedly to their advisors and course lecturers, which could be discussed and explained earlier, in which the faculties are spending too much time responding to their advisees' or students' questions. This design intends to provide cognitive services by identifying the benefits, challenges, and various ways of implementation. All the student-related information can be implemented as a real-time conversational system on the web or on a mobile platform of any HEI, such as institutional websites, mobile apps, or integrations with their social media like WhatsApp, Twitter, Facebook Messenger, or Instagram. It is aimed at responding to students' frequently asked questions (FAQs) about pedagogical information, including course registrations, fees, exams, course assessments, grades, appeals against the grades, and anything related to students in a higher education institution (HEI). The proposed student support chatbot is a framework model that enables the discovery of theoretical and conceptual design.
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