Responses from Kasper can result in different formats of data. The application extracts the slots and intents and then proceeds to build the query. The back-end application then receives the response from the lambda function, segregates the JSON, and classifies information into the corresponding intent and slots. The JSON response from the lambda function contains the input transcript, intent, and slots information. Natural Language to SQL ConversionĪll the responses that require output from the database are sourced with the help of a lambda function. Here ‘cola’ and ‘operatora’ are slot variables under custom slots ‘columnname’ and ‘operator’ respectively. In the utterance section of Kasper, it was recorded as below: For example, there was a query – “show clients where invoice amount is greater than 20000”. After adding intents, its corresponding utterances, and slots, a few slots need to be added as custom slots. Like all bots, Kasper is also built on intents, utterances, and slots. Kasper is a chatbot built specifically for a lending platform to retrieve various data points based on specific inquiries. The following components of this blog will give a clear understanding to the user, how everything is built, networked, and coupled with a custom user interface. This blog discusses how information can be retrieved from databases with a simple question asked to Kasper (the name of our bot). The intent fulfillment, dialogue flow, and context management features of Amazon Lex help to make conversation with a chat-bot as human-like as possible. Amazon Lex offers features that tackle several complexities faced while building the previous generation of chatbots. Bots themselves have gradually evolved from typical question-answering bots to more complex ones that can perform an array of functions. It has redefined how people in the industry perceive building chat-bots. Amazon Lex – Machine Learning As a ServiceĪmazon Lex is one service that enables state-of-the-art chatbots to be built. AWS technology and tools open several avenues to make this possible. There is a critical need for one central point from which a variety of data can be delivered to the user in an efficient and effective process. What customers find difficult is digging out the specific report or data needed through a multitude of mouse-clicks and then spending a lot of time analyzing them. In several fintech applications, information is made available through reporting solutions, presentations, charts, etc. Incorrect decisions can lead to severe consequences or lost customers. Decisions will have to be made accurately and fast.
Especially in a customer-driven market like fintech, “ time is money ”.
In this fast-paced digital age, organizations need a fast and efficient way of gathering information. Enabling Business Intelligence in Chatbots with Amazon Lex
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