What is Rasa Framework? (10 Reasons to Choose Rasa Framework)

Are you looking for something that helps you handle numerous customer queries on a daily basis? A powerful, feature-rich AI chatbot should be one of your top choices. To help you with it, we have discussed an open-source framework, Rasa in this blog. We have listed 10 reasons why businesses have been opting for Rasa framework. Check it out to understand how it can benefit your business to have an efficient and customizable artificial intelligence chatbot.

Are you tired of handling too many customer queries? Do you think you spend a lot of money on customer executives? Do you want a solution that helps you handle all the customer queries and help you analyze the data, in turn growing your business? 

If your answer is yes, welcome aboard!

Our AI developers have been using the Rasa framework for developing AI chatbots that can solve all the problems mentioned and do more to help you. If you are curious about it, let’s dive right in and understand what is the Rasa chatbot framework.

What is Rasa Framework?

Rasa is an open-source framework that helps businesses improve their conversation and interaction with their customers or audience. It is based on natural language understanding, dialogue management and interactions.

Using Rasa, it becomes easier to build conversational AI and improve it over time. The best part? It follows the interactive learning method for improving which makes it easier for you and saves you time, money and resources. It can be used to build great text- as well as voice-based virtual assistants efficiently. In fact, Rasa claims that they provide standard infrastructure for AI chatbots so that all developers and companies can easily understand and develop one of their own virtual assistants. 

Now that you have a better understanding of the Rasa AI chatbot platform, let’s take a look at the top 10 reasons to choose Rasa’s open-source chatbot framework for your conversational chatbot development.

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10 Reasons To Choose Rasa Framework For Your AI Chatbot

  1. Easy to integrate and customize

    Rasa is an open-source platform for developing conversational AI. Open-source platforms are software with source code that anyone can inspect, modify or enhance. Being open-source, developers will be able to integrate additional features and functionalities as per your requirements. The platform is easy to customize and flexible, hence, it can be modified as per your needs. Being so easy to integrate and customize, it saves your business money and also helps you get exactly what you want.

  2. No state machines

    A state machine stores data or instructions that you feed it. It then gives the result as per the instructions. However, Rasa does not work like a state machine. Being an AI chatbot, it will be conversing with real people. The conversations will also act as data for the chatbot. The artificial intelligence chatbot will use this data as instructions or ‘input’ and will learn from the same. Then, it will modify the future results according to the input. This will prevent you from having to input instructions over and over again thus saving you time, money, energy, and resources. Moreover, this will ensure that your AI chatbot developed using Rasa always remain up-to-date and solves most of the queries of your customers.

  3. Integrate into existing systems

    Another good thing about open-source platforms is that they can be integrated into existing systems without any hassles. This feature helps you leverage all the benefits of various backend systems too. If there are any existing APIs or RPAs, then you integrate the AI chatbot that you have developed with Rasa’s open-source platform. When you make use of such technologies in your conversational AI, it will help you automate various processes easily.

  4. Run it on your favourites

    Where would you like to deploy and run your AI chatbot? By using Rasa for your AI chatbot development, you get the option to run the chatbot on your favorite platform. Our AI chatbot developers can deploy the chatbot application on your premises, or on the cloud easily. If you have some other platform that allows us to deploy the chatbot, then we assure you that we will be able to help you out with that too. Hence, you don’t need to adjust and can run the conversational AI on whichever infrastructure you prefer.

  5. Supports various intents

    Using Rasa to develop an AI chatbot will make sure that your bot actually understands messages rather than just replying with answers that have been fed to it. It allows you to turn free-from text in any language into structured data.

    Moreover, it supports both single and multiple intents. Intent refers to a user’s intention, it helps the chatbot understand what the user wants, why they are messaging and how the problem can be solved. An AI chatbot developed with Rasa also supports both pre-trained and custom entities to help modify intent as per the user’s request.

  6. Interactive learning

    The AI chatbot developed with Rasa works on the basis of interactive learning. Even if you do not have sufficient data for training the artificial intelligent chatbot, it is easy to generate the data by simply talking to the Rasa chatbot demo in the initial stages of the development process. Since it will be in the demo stage, it will be easy to correct any errors made by the chatbot. The AI chatbot developers can provide data to it and work on the bot in the interactive learning method.

  7. Connect with other messaging apps

    It is possible that you have a team answering the queries of the customers on a particular channel like Facebook, Slack, or some other custom channels. If you are creating a custom AI chatbot, then there is a possibility that you may need to launch it on a new channel. This will cause a disruption for your customers who are already used to sending their queries on one channel. To avoid this, the Rasa chatbot framework allows you to create AI assistants that can connect with other messaging apps like Facebook, Google Home, Rocket, Slack and others.

  8. Multiple deployment environments

    It is imperative for product development teams to maintain a workflow to remain relevant in the market. It keeps them productive and helps them deliver a product that is stable and of excellent quality, in a timely manner.

    AI developers could set up development, staging and production environments for optimum results. The development environment acts as the primary defence against bugs because the code is iterated continuously in this environment until it is ready for the next stage of testing. The staging environment is for further testing after the AI developers are sure of their code. Any bugs are reported back to the developers and the cycle continues till the software is free of bugs. Once the code has been tested thoroughly and is completely bug-free, it will then be released to the production environment so it end-users of the AI chatbot.

  9. Analytics and reports

    What is the use of any activity without understanding the benefits? Carrying out certain tasks or functions without studying analytics is the same. It is important to see if the efforts are actually fruitful or not and for that, it becomes crucial to study and understand the analytics behind it.

    Rasa provides the option to check various analytics and data of your artificial intelligence chatbot. The open-source platform also allows you to generate reports that help you understand how your users are engaging with the AI chatbot and vice versa. Now, let’s move to the last reason. 

  10. Role-based access control

    Sales, marketing, customer service, and many other teams in your company will require access to the backend of your artificial intelligence chatbot. The AI chatbot developers may find it risky to give complete access to everyone for fear of tampering of data by mistake or intentionally. To prevent this from happening and providing access to the teams, Rasa allows you to give role-based access control to individuals. An AI developer could simply create new profiles and give the profiles limited access as per their needs. This will ensure the safety and protection of your data, without causing any hindrance to the various operations of your business. 

Still, do you have questions about Rasa? Don’t worry you can check out the frequently answered questions section to get the details.

FAQ About Rasa

  1. Which are the two core components of Rasa?

  2. In Rasa architecture, there are two primary components, first is NLU (Natural Language Understanding) and dialogue management is second. NLU model works with the intent classification, entity extraction, and response retrieval part. While dialogue management determines the future action in a conversation context.

  3. Which companies are using Rasa platform?

  4. There are several companies that use Rasa and a few of them are ERGO, Orange, Lemonade, and T-Mobile.

How Can I Help You Today?

After reading about the important features and reasons to choose Rasa, you might be having questions like what is the cost to build an AI chatbot or how long does it take to develop conversational AI using Rasa. These are genuine questions being an artificial intelligence solution development company, we can help you by providing the answers to your queries.

We are based in Canada and we have worked on over 1500 projects worldwide. We have built a range of websites and apps in various categories and gained expertise in software development and in providing advanced solutions that help solve various problems faced by our clients.

To share your ideas, queries or doubts in complete confidentiality, you can contact us. All you have to do is fill the form given in the footer and one of our sales representatives will get in touch with you within 2 working days. Do not forget and ask for a 30-min free consultation from our expert consultants. 

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Rakesh Patel

Written by

Rakesh Patel is the Founder and CEO of Space-O Technologies (Canada). He has 28 years of IT experience in business strategies, operations & information technology. He has expertise in various aspects of business like project planning, sales, and marketing, and has successfully defined flawless business models for the clients. A techie by mind and a writer at heart, he has authored two books – Enterprise Mobility: Strategy & Solutions and A Guide To Open311

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