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Chatbot Development

Chatbot Development

A chatbot is a piece of software that conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, although as of 2019, they are far short of being able to pass the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatbots use sophisticated natural language processing systems, but many simpler ones scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.

The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs. Today, most chatbots are accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities.

Beyond chatbots, Conversational AI refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale.

The process of building, testing and deploying chatbots can be done on cloud-based chatbot development platforms offered by cloud Platform as a Service (PaaS) providers such as Oracle Cloud Platform SnatchBot and IBM Watson. These cloud platforms provide Natural Language Processing, Artificial Intelligence and Mobile Backend as a Service for chatbot development.

Some Companies like Microsoft Azure and AARC are currently providing their Bot Engines through which chatbot Platforms or Software can be developed.

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Characteristics

Languages

Zapier Integration

Stripe Integration

NLP

Industry Support

Web application

Facebook Messenger support

Telegram Support

Skype Support

WhatsApp Support

Viber Support

WeChat Support

Slack Support

ZenDesk Support

Twilio Support

SMS Support

Twitter Support

Voice Support

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The most popular products in category Chatbot Development All category products

Suppliers Chatbot Development

Aivo

Aivo

Aivo, founded in 2012, develops customer support software solutions with artificial intelligence technologies that transform the way companies and... Read more
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Beep Boop

Beep Boop

Beep Boop is a company that created a ridiculously simple hosting platform for bots. Read more
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Botsify

Botsify

Botsify is a company that created a platform for people and businesses to make their own smart Chatbots without knowing how to code. People... Read more
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F.A.Q about Chatbot Development

New tools designed to simplify the interaction between humans and computers have hit the market: Chatbots or Virtual Assistants. In banking, chatbots and virtual assistants are some of the industry’s newest tools designed to simplify the interaction between humans and computers.

What is a chatbot?

A chatbot is artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone.

Why are chatbots important?

A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. However, from a technological point of view, a chatbot only represents the natural evolution of a Question-Answering system leveraging Natural Language Processing (NLP). Formulating responses to questions in natural language is one of the most typical examples of Natural Language Processing applied in various enterprises’ end-use applications.

How does a chatbot work?

There are two different tasks at the core of a chatbot:

  1. user request analysis
  2. returning the response

User request analysis: this is the first task that a chatbot performs. It analyzes the user’s request to identify the user intent and to extract relevant entities.

The ability to identify the user’s intent and extract data and relevant entities contained in the user’s request is the first condition and the most relevant step at the core of a chatbot: If you are not able to correctly understand the user’s request, you won’t be able to provide the correct answer.

Returning the response: once the user’s intent has been identified, the chatbot must provide the most appropriate response for the user’s request. The answer may be:

  • a generic and predefined text;
  • a text retrieved from a knowledge base that contains different answers;
  • a contextualized piece of information based on data the user has provided;
  • data stored in enterprise systems;
  • the result of an action that the chatbot performed by interacting with one or more backend application;
  • a disambiguating question that helps the chatbot to correctly understand the user’s request.

In what is the benefit of chatbots?

Chatbot applications streamline interactions between people and services, enhancing the customer experience. At the same time, they offer companies new opportunities to improve the customer's engagement process and operational efficiency by reducing the typical cost of customer service.

To be successful, a chatbot solution should be able to effectively perform both of these tasks. Human support plays a key role here: Regardless of the kind of approach and the platform, human intervention is crucial in configuring, training and optimizing the chatbot system.

Which chatbot application is right for you?

There are different approaches and tools that you can use to develop a chatbot. Depending on the use case you want to address, some chatbot technologies are more appropriate than others. In order to achieve the desired results, the combination of different AI forms such as natural language processing, machine learning, and semantic understanding may be the best option.

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