Why does RASA have an edge over other Chatbot Frameworks for implementing Conversational AI & Chatbot Solutions?

In the advent of technology and the digital world, engaging with customers proactively has become a prime goal for enterprises for improving customer service & support, marketing, sales, and generating leads. Gone are the days when all these tasks were outsourced by enterprises to BPOs and Call Centers; not only due to the increased amount of overhead cost but also due to the working hours of these BPOs and Call Centers that used to hinder the process as a whole. This is where the role of Conversational AI & Chatbot comes into play. Before we proceed further, we would like to ascertain the fact that Conversational AI & Chatbots are two different entities and both must not be confused!

Chatbots are software programs with hardcoded logic (possibly if/then statements) with no capacity for learning. Chatbots don’t understand sentences. Instead, they look for specific words that the customer types, and then provide an automated response to those words.

In a food ordering app, which has a menu-based system where you can direct customers to give specific responses, and that, in turn, will provide pre-written answers or information fetch requests.

Conversational AI, on the other hand, Conversational AI is a subset of chatbots that leverages the power of Natural Language Processing (NLP) to determine customer intent behind the message and mimic a human-like conversation with the customer.

In an E-commerce website, a conversational AI interface can help customers navigate to recommended products based on their search history using a human-like conversation.

In one sentence the above fact can be explained as

“Conversational AI powers chatbot. But not all chatbots use Conversational AI”

Now, the question comes as to which framework a developer should use for developing Conversational AI-based chatbots? Well, there are dozens of frameworks available at present which one can use to develop such solutions but what stood out to the majority of developers and enterprises was RASA. The reason why RASA has an edge over other AI-based chatbot frameworks is explained below.


Open Source

RASA is an open-source Conversational AI framework for building AI-powered chatbots, meaning the code is readily available to use for free. It is an accessible, flexible, powerful, and transparent framework. Alongside that, being an open-source product, it has huge community support from developers all around the world. RASA also provides proper well-defined documentation which a developer can always refer to in case of any confusion.

Scalable Framework

One thing that is best about RASA is that it follows a modular, extensible, microservices architecture that fits well in a typical software development scenario. RASA also provides an easy setup with CI/CD Deployment tools like GitHub Actions when taking the bot to production.

Easy Integration

RASA provides easy integration capabilities using REST API channels with popular messaging services like Whatsapp, Slack, Telegram, Facebook Messenger, Rocket.Chat etc Other than that, one can also host the chatbot on their dedicated website easily.


An AI assistant can be classified broadly into 5 types based on their activity levels. Those are:

  • Level 1- Notification Assistants
  • Level 2- FAQ Assistants
  • Level 3- Contextual Assistants
  • Level 4- Personalized Assistants
  • Level 5- Autonomous Organization of Assistants

(Note: Level 4 and Level 5 assistants still remain a fiction)

RASA is a Level 3- Contextual Assistant, unlike any other chatbot framework that is available in the market. RASA believes in the idea of Context matters: what the user has said before is expected knowledge. Considering context also means being capable of understanding and responding to different and unexpected inputs.

State-of-Art NLU

RASA uses DIET (Dual Intent and Entity Transformer) as part of its NLU architecture. DIET is a multi-task transformer architecture that handles both intent classification and entity recognition together. DIET improves upon current state-of-the-art architectures and is six times faster to train along with the ability to plug-and-play various pre-trained embeddings like BERT. It also has support for custom components and pipelines to use any other ML model.


Another perk of using the RASA framework is that it is secure. RASA can be hosted anywhere like cloud or on-premise. RASA applications can be containerized using Docker and can be hosted anywhere as per requirement freely. This solves the dependency of using a particular cloud platform to use specific services like Google Cloud for Dialog Flow, Azure for Bot Framework, and so on. RASA not only offers full control over your data but alongside that it also offers a unique power to enterprises operating in countries that have regulations prohibiting personal data from being stored outside of the country. Alongside that, RASA by default provides two authentication methods namely built-in token and JWT-based authentication that is useful for enterprises to keep their customer details safe and secure in the digital ecosystem.


Unlike any other chatbot framework, RASA provides the power of customization according to the needs of the enterprises.

Pipeline Components: In RASA Open Source, the incoming messages are processed by a sequence of components. These components are described in a “config.yml” file and this is known as a pipeline. By default, RASA provides a standard NLU pipeline configuration that works well in small-scale projects but as your chatbot grows, it is expected that you need to change the pipeline configuration as per your need. RASA offers the power of customization in pipeline components.

Policies: Policies are a set of actions that a chatbot assistant looks forward to taking at each step in a conversation. RASA provides the developer with the power of customizing and choosing the policy according to their need. A set of policies offered by RASA includes Machine Learning Policies (that inherits the power of decision making using machine learning and NLP), Rule-Based Policies (that sticks to particular business logic), Custom Policies (written with reference to the needs of the users).

Development and Debugging

Conversation Driven Development(CDD)- The challenge in conversational AI-based chatbot development is that it is really challenging to anticipate what the users might say while having a conversation with the chatbot. In short, it is “Ambiguous”. To counter this limitation, developers use CDD while building their chatbot assistants. CDD is the process of listening to user messages and generating insights to improve the chatbot assistant. CDD is more like a recursive process than a linear process; which means you’ll circle back to the same actions over and over as you develop and improve your bot.

CDD using RASA follows the following actions:

  • Share your assistance with users as soon as possible.
  • Review conversations on a regular basis.
  • Annotate messages and use them as NLU training data.
  • Test that your assistant always behaves as you expect.
  • Track when your assistant fails and measure its performance over time.
  • Fix how your assistant handles unsuccessful conversations.

RASA X: RASA X is a tool that provides the developer to implement the power of Conversation Driven Development (CDD). RASA X is built on top of RASA open source and comes with a clean and clear Graphical User Interface (GUI) to ease the process of CDD. It has a great interactive bot training feature.

Enterprise Support

Other than free and open source products like RASA Open Source and RASA X, RASA also provides a special product named RASA Enterprise that comes with some more inbuilt power alongside the features already mentioned above. RASA Enterprise is an enterprise-ready subscription to develop and ship contextual assistants at scale.

Some additional powerful features provided by RASA Enterprise include:

  • Analytics: This in-built feature allows enterprises to generate reports on how their chatbot is engaging with the user. It also generates some crucial analytics reports that are beneficial for the enterprise to draw their benefit curve.
  • Role-Based Access Control: Role-Based Access Control in RASA is a feature where only a minimal amount of access is provided to various members/groups of a development team in a company to prevent tampering with data by mistake or intentionally.
  • Deployment and Installation Support
  • Expert Support

Trusted By Leading Enterprises

The world’s leading companies are creating the best AI assistants with the Rasa. Check out their customer base here.

Latest Improvement

Deprecation of the state machine

Today most of the chatbot systems in the industry are still implemented using rule-based(state machine) approaches in the production environment. If/then statements are used in their dialogue management to have a conversation with the chatbot. On the contrary, RASA uses stories that form training data and is further used to train the RASA Core ML-based Dialogue Management models. Stories are descriptions of actual conversation that takes place between a user and a virtual assistant. These Stories are converted into a specific predefined format where user messages are classified into equivalent intents (or entities) and the responses from the AI assistant are classified under the action category.

Getting Rid of Intents

Beyond a point, it gets painful to manage lots of intents because it is hard to realign intent categories with the growing scope of the bot project. And in reality, not every user message will fit into one predefined intent hence it is always challenging to define the NLU intent training data to accurately understand the user’s intention. To get out of this deadlock situation, Rasa has taken the first step getting rid of intents(or making them optional) and training the bot with end-to-end learning.

Advance Entities

Rasa has a more evolved entity configuration where apart from entity labels and synonyms it has roles and groups to provide additional labels to training data to define certain concepts and make AI assistants more accurate.

We threw light on the overall features of the RASA Framework for chatbot development. If your next bot needs custom development and other important features then RASA can be the right choice over any other framework for chatbot development because of its scalable, secure, high performing, and customizable features. Leveraging the power of Machine Learning and Natural Language Processing to solve a production-level conversational AI problem at hand is what RASA is committed to.

“With great power comes great responsibility” — Peter Parker Principle, Spider-Man Comic

And RASA to date has laid with this proverb in the field of conversational AI Development.

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