A ChatBot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.-Wikipedia

In simple words, Chat bot is basically an AI(Artificial Intelligence) who communicate to the humans using text or text to speech. The chat bot responds to the question asked by humans in the chat window of the web page or application.

For several years chatbots were typically used in customer service environments but are now being used in a variety of other roles within enterprises to improve customer experience and business efficiencies.

How a chatbot works?

A chatbot is an one of the most advanced and promising expressions of interaction between humans and machines. From a technological point of view, a chatbot represents the natural evolution of a Question Answering system leveraging Natural Language Processing (NLP).

On other side Human interact with chatbot and then only chat bot started working.

If voice is used, the chatbot first turns the voice data input into text (using Automatic Speech Recognition (ASR) technology). Text only chatbots such as text-based messaging services skip this step.

The chatbot then analyses the text input, considers the best response and delivers that back to the user. The chatbot’s reply output may be delivered in any number of ways such as written text, voice via Text to Speech (TTS) tools, or perhaps by completing a task.

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 

Driven by AI, automated rules, natural-language processing (NLP), and machine learning (ML), chatbots process data to deliver responses to requests of all kinds. 

There are two main types of chatbots:

Task-oriented (declarative) chatbots are single-purpose programs that focus on performing one function. Using rules, NLP, and very little ML, they generate automated but conversational responses to user inquiries. 

Interactions with these chatbots are highly specific and structured and are most applicable to support and service functions—think robust, interactive FAQs. Task-oriented chatbots can handle common questions, such as queries about hours of business or simple transactions that don’t involve a variety of variables. 

Though they do use NLP so end users can experience them in a conversational way, their capabilities are fairly basic. These are currently the most commonly used chatbots. 

Data-driven and predictive (conversational) chatbots are often referred to as virtual assistants or digital assistants, and they are much more sophisticated, interactive, and personalized than task-oriented chatbots. 

These chatbots are contextually aware and leverage natural-language understanding (NLU), NLP, and ML to learn as they go. They apply predictive intelligence and analytics to enable personalization based on user profiles and past user behavior. 

Digital assistants can learn a user’s preferences over time, provide recommendations, and even anticipate needs. In addition to monitoring data and intent, they can initiate conversations. Apple’s Siri and Amazon’s Alexa are examples of consumer-oriented, data-driven, predictive chatbots. 

Why are Chatbots so Popular?

Smartphones, wearables and the Internet of things (IoT) have changed the technology landscape in recent years. As digital artefacts got smaller, the computing power inside has become greater. 

But mobile apps and data-heavy activities don’t go hand in hand. Wading through complicated menus isn’t the fast and seamless user experience businesses need to deliver today. 

In addition, consumers are no longer content to be restricted by the communication methods chosen by an organization. They want to interface with technology across a wide number of channels. 

Chatbots offer a way to solve these issues by allowing customers to simply ask for whatever they need, across multiple channels, wherever they are, night or day. 

Types of Chatbot Technology

The majority of chatbot development tools today are based on two main types of chatbots, either linguistic (rule-based chatbots) or machine learning (AI chatbot) models. 

Linguistic Based (Rule-Based Chatbots)

Linguistic based – sometimes referred to as ‘rules-based’, delivers the fine-tuned control and flexibility that is missing in machine learning chatbots. It’s possible to work out in advance what the correct answer to a question is, and design automated tests to check the quality and consistency of the system. 

Rule-based chatbots use if/then logic to create conversational flows. 

Language conditions can be created to look at the words, their order, synonyms, common ways to phrase a question and more, to ensure that questions with the same meaning receive the same answer. If something is not right in the understanding it’s possible for a human to fine-tune the conditions. 

Machine learning (AI Chatbots)

Chatbots powered by AI Software are more complex than rule-based chatbots and tend to be more conversational, data-driven and predictive. 

These types of chatbots are generally more sophisticated, interactive, and personalized than task-oriented chatbots. Over time with data they are more contextually aware and leverage natural language understanding and apply predictive intelligence to personalize a user’s experience. 

Hybrid Model – The Ultimate Chatbot Experience

While linguistic and machine learning models have a place in developing some types of conversational systems, taking a hybrid approach offers the best of both worlds, and offers the ability to deliver more complex conversational AI chatbot solutions. 

A hybrid approach has several key advantages over both the alternatives. When considered against machine learning methods, it allows for conversational systems to be built even without data, provides transparency in how the system operates, enables business users to understand the application, and ensures that a consistent personality is maintained and that its behavior is in alignment with business expectations. 

Common Chatbot Uses: 

Chatbots are frequently used to improve the IT service management experience, which delves towards self-service and automating processes offered to internal staff. 

With an intelligent chatbot, common tasks such as password updates, system status, outage alerts, and knowledge management can be readily automated and made available 24/7, while broadening access to commonly used voice and text based conversational interfaces. 

Are Chatbots Bad? 

There are some misconceptions about the term chatbot. Although the terms chatbot and bot are sometimes used interchangeably, a bot is simply an automated program that can be used either for legitimate or malicious purposes. 

The negative connotation around the word bot is attributable to a history of hackers using automated programs to infiltrate, usurp, and generally cause havoc in the digital ecosystem. 

Bots and chatbots, therefore, should not be confused. Generally speaking, chatbots do not have a history of being used for hacking purposes. Chatbots are conversational tools that perform routine tasks efficiently. 

People like them because they help them get through those tasks quickly so they can focus their attention on high-level, strategic, and engaging activities that require human capabilities that cannot be replicated by machines. 

The Value of AI Chatbots for Business: 

The Best AI Chatbot for the Enterprise.Users value chatbots because they are fast, intuitive and convenient. For enterprises, AI chatbots offer a way to build a more personalized and engaging customer experience, which in return delivers a wealth of customer information that is highly valuable in better understanding their customers and growing their business. 

Here are the 10 key areas where businesses can derive value from chatbots: 

Immediate Response

Drive More Revenue

Reduce Costs

Maximize Staff Skills

Reach New Channels

Increase Loyalty

Available 24/7

Increase Engagement

Understand the Customer Better

Build Differentiation

Chatbots help deliver a frictionless user experience that drives product differentiation through innovation, new levels of customer engagement, and an intuitive and fast interaction. By 2020 customer experience will overtake price and product as a key differentiator.