In this business growing world, this phrase generally keeps us thriving- . Change is constant This change can be perceived by studying our customers based on their buying pattern, buying behavior, need for personalized services, after service assistance, and so on. By understanding them various brands started testing the capability of an AI-powered chatbot to provide a personalized experience, real-time conversations, and anticipate customer needs. What makes it possible for the brand to do all this is definitely a chatbot but the core of it lies in Artificial Intelligence. This blog will help you understand why AI needs to be an integral part of the chatbot development process. A chatbot is now majorly performing activities like answering FAQs, providing assistance, and solving queries triggered by the user. What is important while conducting such conversations is to maintain a human touch till the end. What do I mean by human touch? Let us look at the following points to see what happens in a human to human conversation? In an h2h conversation, the spoken words are understood clearly. We don’t talk two different things at the same time (except when there is an argument). We maintain the context of our conversation. Understand the major during the conversations. pain points All these points make the conversation understandable, pleasant, and flowing in a direction. Chatbot popularized for its ability to strike conversations with the user, the above-mentioned points plays an imperative role to provide a better user experience making the conversation look more human than robotic. The bot can effectively perform such activities with the help of Artificial Intelligence. AI gives the bot the ability to think and talk like a human. Artificial intelligence consists of two major elements that are Natural language Processing and Machine Learning. They are interconnected terms working with the same purpose of providing a high quality of output. is an algorithm that helps the bot to learn from queries/data. When a query gets triggered, Machine learning helps the bot to first monitor the past conversation it had with the user and give a response accordingly. Machine learning is popularly known as NLP, which provides assistance to the bot to understand and interpret the information closely. With NLP, you train your chatbot with various intents that the user will type during the conversation and these intents will streamline the response to the query. Natural Language Processing The combination of NLP and Machine learning gives you a first-hand data of understanding customer needs. But here the question might hit that how can we use this technology? or how do they have to set this up? With technological advancement, there is no need for you to create machine learning tools all you need to do is deployed an AI-powered chatbot using chatbot development platforms. A chatbot development platform helps you to build a chatbot based on your requirements. Floatbot is an AI-powered chatbot development platform that allows you to select your NLP engine between itself and Dialogflow. You can use the NLP of Dialogflow and enjoy the benefits of our platform. Floatbot as an AI provider lets you trained your bot at Basic level of AI and Advance level of AI. With Basic AI you train your bot to answer FAQs and regular queries, where the response can be set in form of a text or a flow. With advance AI the bot is trained with various user intents and entities depending upon what your user might say. Floatbot dashboard- Basic AI Floatbot dashboard- Advance AI AI helps to improve the customer service as it makes the chatbot able to understand the user query closely and trigger a response that solves the issue. Solving the issue is the major focus for most of us and with AI that becomes possible. With the help of chatbot development platform including AI in your bot is no more a rocket science, you can start by following our handy guidelines that will help you to build a and get advance with your development process by going through our . chatbot from scratch documentation page

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