Consumers are like delicate glass that is supposed to break when not handled carefully. There are countless products that a specific company provides, of which, the origins lay in the customer’s conduct, their reviews for your brand, the belief factor, and how your brand gratifies customers. To marinate this tremendously expectant market when the aim is to offer immediate support, exact solutions, and drive discussions then chatbots supercharged with artificial intelligence come in demand. In this blog, you will find out what a chatbot is, and the frameworks used to develop chatbots.
Chatbot — An Overview
A chatbot is a software application used by websites and applications, some of them are developed using angular coding standards. The application can involve in discussion with a human and comprehending their needs. And after getting the person’s point, chatbots help them to attain the desired results. Building great Customer Retention Management requires a lot of time and effort as well. However, the chatbot helps you to scale up your company cycle and also handles the CRM routine like a pro. This application understands human language and responds to clientele like a real individual is speaking to you.
Image Demonstrates Chatbots Market is Thriving in Every Niche
Chatbot statistic You Should Know
- 90% of industries report an improvement in the speed of customer grievance resolution.
- 80% of chatbot users have reported they have an optimistic experience with chatbots.
- 23% of consumer service organizations are using AI chatbots as a communication conduit.
- 69% of customers like the chatbot services as they get instant responses.
- Giants like Starbucks, eBay, LinkedIn, British Airways, and others are enduring to use chatbots in 2023.
- Chatbots help businesses condense call, email, and chat inquiries by 70%.
- 60% of millennial reports the usage of chatbot.
- The chatbot market is expected to reach $454.8 million in revenue by the year 2027.
- 25% of tourism and hospitality companies globally use chatbots.
As of now, you are familiar with chatbots and their market, so, let’s explore the frameworks used to create chatbots.
7 Frameworks to Build Powerful Chatbots
Chatbot framework is where bots are built, and their conduct is defined. The bot development frameworks abstract away much of the manual effort that is intricate in emerging chatbots. However, the chatbot framework brags “Write once install anywhere”, and you are more probable to develop a distinct chatbot for your messaging platforms. Following are a few frameworks to build this powerful messaging application software.
It is an open-source framework used to build chatbots. The framework is typically utilized by the government, insurance companies, and corporates that deliver monetary services. Bot press provides on-site chatbots that improve confidentiality unlike when using cloud-based chatbots.
Pros of Botpress
- It is simply customizable.
- The documentation delivered is easy to comprehend and use.
Cons of Botpress
- It offers restricted features.
- The framework usages high learning curves.
This chatbot-creating framework uses the AI Markup Language (AIML). You can build AI-compelled virtual mediators to support human-like text or voice conversations for customers. This framework supports multi-language, cross-channel, voice-enabled, lithe, and extensible RESTful APIs. The industries such as entertainment, e-learning, education, and virtual assistant are using the Pandora Bots framework.
Pros of Pandora Bots
- Effortlessly add speech to text and vice versa.
- Deploy to messaging or voice channels.
- No podium locks in.
Cons of Pandora Bots
- Less accurateness.
- Need to learn AIML.
It is an open-source framework for developing contextual chatbots. As there are many frameworks obtainable but most of them offer elementary functionalities like replying to static FAQs or predefined questions. However, the RASA framework offers more than that. It builds a more sophisticated chatbot and offers the feeling of human collaboration.
RASA has two major elements RASA NLU and RASA Core. RASA NLU is accountable for understanding the natural language and RASA core plays a vital role in creating intellectual, conversational contextual chatbots.
Pros of RASA
- RASA builds enormously customizable chatbots. The creator can employ several pipelines to process discussions with the customers.
- The chatbots can run as simple HTTPS servers.
Cons of RASA
- Chatbots built by the Framework are resource intensive on the server side.
- It does not endow straight incorporation with messaging podiums straight out of the box.
It is an open-source chatbot development framework introduced by Facebook. The framework is used to create applications and devices that customers can talk to. It facilitates users to use their voice to control appliances, lighting, smart speakers, and more. This chatbot-building framework uses the Wit.ai NLP engine to comprehend customer’s intent and provide valuable information
Pros of Wit.ai
- Support more than 80 languages.
- Easy to deploy.
- Integrates with messengers, wearable devices, etc.
Cons of Wit.ai
- Learning the NLP engine in Wit.ai is difficult.
- Challenging to recover missing parameters.
IBM Watson framework utilizes modern technologies such as machine learning and artificial intelligence. This chatbot developing framework uses Watson AI, Machine learning, and Natural Language Understanding to cram from earlier patron conversations.
The framework lets enterprises retain data that flows through it. This is a distinctive feature since other trademarked vendors of chatbot frameworks gather the information assembled by their chatbots.
The framework’s confidentiality offer isolates the information collected by their assistants in a secretive cloud. This is done to save proprietary perceptions acquired from consumer interaction.
Pros of IBM Watson
- The framework security policies promote data privacy. Data privacy has become a huge concern in this era of technical advances, and IBM is leading the change.
- It lets unified phone integration. When the chatbot gets a request that it cannot resolve, it joins the client to a telephony platform to get further help.
Cons of IBM Watson
- The framework does not permit the end user to get chat history.
- It is a complex framework.
This chatbot-building framework is offered by Amazon Web Services that uses Artificial Intelligence. The framework incorporates several technologies offered by Amazon to aid in its functionality. It uses Amazon Cognito for the consumer verification process. It then uses Automatic Speech Recognition to translate audio into text. This framework utilizes Amazon Polly Services for transforming text to human speech. The interconnection of numerous technologies improves the functionality of chatbots built using this framework.
Pros of Amazon Lex
- The framework supports various podiums and disposition of them is through one click process.
- It has automatic scaling competencies.
Cons of Amazon Lex
- It is not multilingual as it supports English only.
- The data planning process is very complex.
The framework allows developers to build intelligent chatbots that comprehend various language dynamics as it is supported by Google’s Cloud Natural Language. It is used to develop conversational applications for consumers in different languages and on numerous platforms.
Pros of Dialogflow
- It supports around 20 languages worldwide.
- The framework offers Software Development Kits for 14 platforms.
Cons of Dialogflow
- The framework does not provide live consumer support.
Chatbots are an effective solution to improve consumer services. No matter how well the technology of a business is, if you do not provide good patron support, your company suffers. This is the reason entrepreneurs these days Hire AngularJS developers and are adopting chatbot services at a rapid pace. Hope the above-mentioned chatbot frameworks help you to pick a suitable one for your business.
7 Frameworks to Build Powerful Chatbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.