AI-powered Conversational Chatbot Development Using

Sanam Malhotra | 23rd January 2020

Artificial intelligence (AI) is the driving force behind emerging conversational technologies. Businesses are beginning to strengthen their customer relations with conversational AI technologies by deploying various chatbot development frameworks. One such bot framework, is gaining momentum with its machine learning algorithms to empower chatbots and virtual assistants across applications and IoT devices. As an emerging chatbot development company, we at Oodles AI are exploring new business opportunities for conversational chatbot development using

In this article, we explore how artificial intelligence services can be combined with to develop business-oriented applications.


How Machine Learning is Embedded in

The core functionality of is based on two major ML technologies, i.e. Natural Language Processing (NLP) and Natural Language Understanding (NLU). While NLP enabled to break customer queries into actionable ‘entities’, NLU extracted meaning out of these entities. However, in April 2016, released an entirely new mechanism called Bit Engine, for building  NLP-based chatbots or virtual agents.

The new setup of facilitates the development of cognitive chatbots based on the concept of ‘Stories’. Stories provide the essential conversational flow to human-chatbot interactions using ‘Actions’. Though much of this new paradigm shift in works similar to Watson’s intent and entity mechanism. Application integration of is made simpler with the support of popular programming languages such as Python, Ruby, Go, and Node.js. 

For now, let’s look at the four main pillars that keep a chatbot interaction going in today-

1) User says

The first step is to identify and input the exact query or command you expect your user to raise. 

“I need a 30-minute appointment for a haircut tomorrow at 7 pm”

It prompts to extract the following “Entities” form the text-

Intent  Haircut appointment
wit/duration 30 minutes
wit/datetime 01/24/2020, 7:00 PM


The Understanding tab in enables us to add the variants of this text or input in order to train the chatbot with human-like language.

2) Bot Sends

This section defines the message that a chatbot should send to the user. It may be an answer for a query or a prompt to fetch further information.

3) Jump

As a chatbot developer, it is important to maintain the flow of the conversation. The Jump section enables developers to jump at any point in the user-chatbot conversation and create bookmarks for important exit points.

4) Bot executes

It is the final control wherein embeds ‘Action’ into the chatbot interface. Here, developers can instruct the bot to execute certain actions wherever required. However, this action runs parallel to the code that is built in the bot’s backend to fulfill the user’s command.

If we take the above haircut example forward, the bot executes section would divide the input between ‘context’ and ‘entities’. Here, the entities mapped in the first section, ‘User says’ will support the bot to provide specific actions. 

Findhairsalon (context, entities)


The context object comprises of keys and values that can be used to instruct Wit when action functions add location, crowd, and budget keys. It leads Wit to demonstrate the action and complete user requests accurately and efficiently.

Also read- Powering Chatbots with IBM Watson to Impact Global Businesses


Different Conversational AI Applications Using

Virtual Assistants

The NLP engine inside provides for yet another virtual assistant framework to integrate with Google Home, Alexa, and other voice-controlled IoT devices. Businesses are beginning to expand their customer services using the voice recognition capabilities of

For instance, real estate businesses are executing conversational chatbot development using to assist their potential customers in locating their ideal house or property. is able to analyze and respond to various customer preferences and needs with valuable information. Moreover, the location access feature in virtual assistants like Google Home can train to find nearby restaurants, hospitals, etc. with ease.


Voice-controlled wearable gadgets

Building voice interfaces for wearable gadgets just got easier with The exhaustive set of HTTP APIs in simplifies the development of voice-controlled wearable gadgets. The applications of wearable gadgets are most commonly used by healthcare service providers. The NLU algorithms inside enables the smart IoT devices to monitor user health, extract insights, and provide suggestions to improve lifestyle. Some businesses are also deploying over the cloud to automate appointment bookings with health assistants.

Also read- Conversational AI Trends 2020: Roadmap and Strategy


Deploying Conversational AI using with Oodles

Conversational AI is one of the most potential developments expected to reach new heights in the new year 2020. At Oodles, we are constantly exploring new technologies to harness the capabilities of IoT devices and artificial intelligence to build intuitive conversational interfaces. Our AI team has experiential knowledge in deploying Natural Language Processing algorithms to empower eCommerce, insurance, and other businesses for optimum customer experience.

Our capabilities with extend to an in-depth analysis of specific user intents and mapping them to build domain-specific chatbots or virtual assistants. We deploy conversational chatbot development using both on-premise or in the cloud by using services like AWS, Google Cloud, and more. 

Reach out to our AI development team to explore our diverse artificial intelligence service.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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