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.
Data science experience required?
Rule-based or AI-powered?
Configuration time (Approximate)?
Chatbot development team?
Developers and conversation designers
Extended team required(data scientists, ML experts, developers, conversation designers, etc.)
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.
Rasa spends a lot of time and energy working on their NLU research, making their models highly customizable and configurable. That might sound like a good thing but, in reality, it means that users need to keep a close eye on the changes to the underlying models. They might even need to rebuild chatbots entirely when technology advances break existing model configurations.
At Botpress, we focus on managing and improving our NLU engine in a way that’s designed to be more evergreen. Chatbots continue to work as we improve things behind the scenes, while our in-depth analytics help you to see the impact on conversations.
In addition, dialogue management is handled quite differently in Rasa and Botpress. Because Rasa is driven by artificial intelligence (AI), conversations can be unpredictable. And, as we’ve covered above, it’s difficult to visualize them. Aiming to offer the best of both worlds, Botpress uses powerful AI in conjunction with more predictable rule-based programming.
Botpress vs Dialogflow - What are the differences?
If you’re a developer with in-depth knowledge of NLP and machine learning, or access to a team of data scientists, Rasa is a solution that’s worth considering.
For a solution that’s simple to get started with, easy to manage, but capable of scaling with your business, check out our managed NLU platform (with 9,500 GitHub stars) by getting started for free today.
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