Rasa vs. Voiceflow

I have been using the platform for over 3 years and I have noticed a very good evolution, in an attempt to reinvent themselves. The support team is amazing, always available to work out with us in achieving the best results. About the technology, the algorithms available in the platform suits most of the cases. Being language agnostic is a very positive point for us, because some big tech platforms have little support for PT-PT language.

Incentivized

For a design team, Voiceflow is a must to have skill. With all the features present and being constantly rolled out by Voiceflow team, the tool has become one of the core part of design systems. Additionally, the support team of Voiceflow is hard-working and dedicated to customer satisfaction which truly brings about a difference. For context, we have raised multiple bugs and asked for best practices and the prompt response of support team from Voiceflow has been instrumental in us using the tool more and more

Incentivized

• Rasa team has Top notch AI knowledge
• Greate customer support, by listening towards the clients needs.
• And building future proof solutions around client Business Requirements within dazzling timeframes

Incentivized

• Rasa CALM flows and Rasa domain could be made fully independent of the Rasa training process and dynamically retrievable from e.g. a graph DB. This would make the chatbot more flexible.
• Prompt templates, or at least paths could be referenced in Rasa config. Different policies in the Rasa config could then be configured without code change to use different prompt templates
• LLM configuration should rather be part of the endpoints, than model configuration.
• Rasa Studio could support all the functionality of Rasa Pro.

Incentivized

With the help of dedicated team - documentation and video resources it is relatively easier to build. We prioritized pro-code usage to begin with launch.

Incentivized

Voiceflow has become an integral part of our design eco-system, providing capabilities for both rapid design and prototyping as well as testing with users. Additionally, the easy API integration has also allowed to create test automations using Gen AI

Incentivized

Rasa support has been very responsive, trying to fix any reported issues ASAP. They've also listened to many requests for improvement. The Rasa features and changelog are well documented

Incentivized

Glean - proprietary semantic search algorithms, no backend actions integration IBM Watsonx - complicated dialogue builder, poor separation of no-code and pro-code interfaces ELMOS (agent based) - all logic in code, no dialogue logic in no-code interface possible Rasa - transparent and simple sharing of objects between no-code and pro-code interfaces. Transparent LLM usage and restrictions. Simple backend integration via Rasa SDK

Incentivized

• Reduced Human Connected Calls Per active User
• Improved Calls disposed by Voice Agent
• Reduced call wait times

Incentivized