Our NLU is what we categorize as a few-shots. It doesn't require a lot of data, sometimes 10 examples for an intent will be enough. It has a direct impact on how fast it trains, but more importantly how fast you can put it in the hands of actual users. This is a huge barrier to entry for developers starting out. If you need 100 utterances per intent just to get started, it might be difficult to come up with a solid proof of concept that you can build on top of. With our platform, you just get it done faster.
Comparisons among chatbot platforms is difficult because brief summaries of what they do can seem very similar. Both Rasa and Botpress products use NLP, offer integrations, and have open-source models.
What sets Botpress and Rasa apart isn’t so much what they do, but how they do it. Below we’ve broken down the key areas in which our offering differs from those of Rasa.
Botpress
Rasa
The Botpress Conversation Studio is a visual design environment created to help you build chatbots quickly and easily. With Botpress, you can start building in less than a minute. Botpress is an end-to-end platform for building chatbots, using a powerful visual flow editor.
It’s embedded with best practices to help you get things right, but you can also use it to write custom logic. If things go wrong, you can use
the built-in Emulator Window to debug conversations and fix errors.
Relying on command line execution, Rasa doesn’t have a comparable visual tool for non-technical users. Its user interface is more complicated and relies on “stories”, which aren’t visualizable.
Unless you understand exactly what you’re doing when you’re configuring, you might find building and deployment to be a struggle. To debug a Rasa chatbot may require leaving the Rasa development environment & workflow.
We created Botpress as an accessible tool for developers looking to build chatbots on their own, without in-depth ML knowledge or the support of a data science team.
Dialogflow is one cog in Google’s Cloud offering. It allows users to build both text and audio chatbots, and comes in two different versions – ES and CX. As such, despite the presence of code templates and starter packs, new users might find Dialogflow more difficult to get to grips with compared to Botpress’ low code drag and drop user interface.
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Answering the same customer or user queries over and over again is not a good use of resources. Using automation and AI in conjunction with NLP is a great way to reclaim some of that time.
Rasa offers highly customizable chatbots built from the ground up, but requires in-depth knowledge of data science. Botpress is more accessible, even to non-technical users.
Botpress uses a managed Language Understanding engine that’s designed to be easy to use. There’s no need for any knowledge of NLP, so any experience you have is just a bonus.
Botpress supports 12 languages – including English, French, Spanish, Japanese, and Arabic – but a FastText model is available in an additional 157 languages. Rasa assistants can be used on training data in any language.
Botpress Enterprise also offers the use of multiple languages simultaneously, as well as built-in translation features.
It’s possible to use Rasa's NLU with the Botpress Conversation Studio.
Botpress integrates with Facebook Messenger, Microsoft Teams, Telegram, WhatsApp and Slack, with support for others in the pipeline. You can also use The Converse API to integrate Botpress with any application or any other channel.
According to their documentation, Rasa currently offers integrations with Facebook Messenger, Slack, Telegram, Twilio, Cisco Webex Teams, Google Hangouts, and a couple of others.
Rasa X is used to provide corrections to improve the predictability of the Rasa chatbot model. A Botpress chatbot has a more rule-based dialogue flow, so the model outcomes can be designed into the bot upfront.
In addition to Botpress Open Source, we have an Enterprise plan that’s tailored to large organizations to help them get the most out of AI when building chatbots.
Craft amazing conversational experiences.