In this blog, We will be discussing the Top 7 Chatbot Development Frameworks.
Chatbots have now become integral to many businesses. They leverage chatbots for the customer support service. The chatbot augments the human agents to deliver customer service support. Businesses get tons of queries every day. Answering them manually not only is time-consuming but also adds to the company’s costs since they have to hire more people for customer support service. Today, the lack of a prompt response usually causes customers to become frustrated. This could ultimately lead a business to lose a customer. That is why having efficient customer service is at the core of every business process.
This is where the use of chatbots comes into the picture. Just imagine how efficient and convenient it will be if there is a bot that answers all the queries of the users. This imagination has been turned into reality using AI.
You must have found chatbots on many websites you visit like an ed-tech website. Did I guess that right? Super! Yes, the chatbot can handle all the queries like inquiries about a course/Bootcamp. The chatbots are made so intelligent that you can even book movie tickets or flight tickets just by instructing the bot. Chatbots leverage the power of NLP (Natural Language Processing) to make it super intelligent.
According to a report by Outlook (2018), 80% of businesses are projected to integrate some form of chatbot system by 2022.
So, this is the right time for you to learn to build chatbots like Alexa or Google Assistant. There are various frameworks available that enable you to build and integrate chatbots seamlessly.
So, without wasting any further time, let us start discussing the Top 7 Chatbot Development Frameworks.
1. Google Dialogflow
Dialogflow is a chatbot development framework owned by Google. It has built-in NLP features enabling users to build NLP-based chatbots. Dialogflow is used to build conversational apps for customers in various languages and on multiple platforms.
Do you know that Malaysia Airlines streamlines the flight search, booking, and payment for its customers using Google Dialogflow? Yes, truly amazing it is.
- Easy to learn
- Supports both text-based and voice-based assistants.
- Manage and Scale with ease
- Multilingual Support
- Integrates with Messenger, Skype, Telegram, Twilio, etc.
- You can only provide one webhook for each project.
2. Amazon Lex
Indeed one of the most powerful frameworks for building chatbots! It has advanced NLP Models for building conversational interfaces into the applications. Amazon Lex manages the dialogue and dynamically adjusts the responses in the conversation.
The American Heart Association engages nearly 1 million participants, nationwide, through the premier Heart Walk events to further their mission of saving lives. AHA is using Amazon Lex to streamline the registration process so that HeartWalk Participants can use their natural voice to easily register through the website.
- Automated Speech Recognition
- Provides SDK for many platforms
- Ability to execute business logic
- AWS Lambda Integration
- It supports only English language
- Complex Web Integration
RASA is a python-based open-source framework. It has two major components: RASA NLU and RASA Core. Rasa NLU is responsible for natural language understanding while Rasa core helps create intelligent, conversational chatbots.
RASA uses Machine Learning models to determine the flow of the conversation. It was named the “Cool Vendor in Conversational AI Platforms” by Gartner.
T-Mobile is the second-largest wireless carrier in the US. At times, over 20k+ customers could be in the queue to speak with a T-Mobile Expert, many with simple requests. That’s why the company thought to build a conversational AI bot that could assist in answering the queries. They brought the development completely in-house so as to save the cost and also they could customize each and every aspect of the bot. So, they used RASA.
- Highly customizable
- Multiple Deployment Environments
- Role-based access control
- Integrates with Messenger, Slack, Telegram, Twilio, etc.
- Not suitable for beginners. Knowledge in NLP is required.
- Fine control over dialogue processing is not available to the programmer.
4. IBM Watson
Do you want a chatbot framework that that can be used even by non-technical users? Or you do not want your data to be shared? If yes, IBM Watson is the go-to framework for building chatbots.
It is built on Neural Network that uses a processing framework to understand and learn conversational cues.
KPMG LLP, a Big Four audit, tax, and advisory firm used IBM Watson to help them more effectively find R&D tax relief for their clients. It helped tax professionals determine tax relief eligibility with confidence.
- Automated Predictive Analytics
- Lets you store data on a private cloud
- Multilingual Support
- Integrates with Messenger, WordPress, etc.
- Slow Integration
- Quite expensive
Wit.ai is an open-source chatbot-building framework built by Facebook. It enables people to use their voices to control smart speakers, appliances, lighting, and more.
Structurely Aisa Holmes chatbot asks users various questions to help the users find a house with qualities and features that meet their specified preferences. It uses Wit.ai NLP Engine to understand user intent and deliver valuable information.
- Easy to deploy
- Large Developer Community
- Offers 80+ languages support
- Integrates with Messenger, Wearable Devices, etc.
- Hard to retrieve missing parameters
- If we share the data, it is shared across the Wit.ai ecosystem
It is an open-source chatbot development framework. It is based on the scripting language of AIML (artificial intelligence markup language), which developers can use to build conversational bots.
Pandorabots is built for developers and CX Designers. It doesn’t have pre-configured Machine Learning tools.
SuperFish AI is a language learning platform used to teach English at scale. They wanted a standardized solution for English language learning in rural areas of China where there is a shortage of English-speaking teachers. By using Pandorabot, Superfish was able to immediately introduce a robust, free-form English language conversation practice partner to supplement their internally developed content and lesson plans. The Pandorabots platform allows them to continually improve and target their chatbot content based on real-time student usage.
- No platform lock-in: own and download your code
- Iterate rapidly: CI/CD, version control, chatlogs
- Deploy to messaging or voice channels
- Easily add Speech-to-text and Text-to-speech
- RESTful APIs to integrate with apps and systems
- Less accuracy
- Have to learn AIML separately
7. Microsoft Bot Framework
Language Understanding (LUIS) is a machine-learning-based service to build natural language into apps, bots, and IoT devices. LUIS interprets user intents and extracts salient details from any request. LUIS also learns as it goes, allowing you to continuously improve the quality of your bot’s conversations.
UPS, a long-time IT innovator has improved customer service with intelligent applications that deliver relevant, seamless experiences to its customers on virtually any device. UPS delivers more than 19 million packages in more than 220 countries and territories. Customers can engage UPS Bot in text-based and voice-based conversations to get the information they need about shipments, rates, and UPS locations.
- SDKs for multiple computer languages
- Enterprise-ready, available worldwide
- Integrates with Cortana, MS Team, Slack, Skype, etc.
- Supports either Node.js or C# for development.
So, in this blog, we discussed the best 7 Chatbot Development Frameworks and their pros and cons
Chatbots have proven to be an extremely effective solution to improve customer services. It is time-saving as well as efficient. Despite how well the technology of the company is, if the customer support is not good, the business suffers. That’s why companies are adopting chatbot services at a very rapid pace.
Let the Data Confess Pvt. Ltd. organizes Bootcamp on “creating your own chatbot from scratch using RASA” along with deployment on the cloud. You can enroll for the Bootcamp if want to learn along with proper guidance.
You can visit our YouTube channel as well to learn more: