Botpress vs. Google Cloud Customer Experience Agent Studio (CX Agent Studio) vs. Rasa

Indicated for those who have good knowledge of programming and coding in HTML and JSON, that is, it is not suitable for beginners or users without much technical knowledge. It is suitable for those who want to integrate with various platforms, such as Telegram, Webhat, Facebook Messenger, among others. I also recommend it to anyone who needs to create a chatbot in several languages (it is not automatic translation). Recommended to create new projects in CX environment and not in ES.

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Rasa Pro is well suited for corporate use and for chatbots which require backend connections. Smaller chatbots with a few flows might be better served with a simple dialogue engine and custom AI agents, or Rasa Open Source. Rasa does not come with its own complex vector database, just in-memory FAISS and connectors to external vector DB's such as Milvus and Qdrant. It provides only a basic document parser and embedder for FAISS. If you need to build a RAG focused chatbot around a large knowledge base with complex documents, e.g. lots of MS Word or PDF files, you'll have to build a separate document parser and embedder, as well as your own semantic search engine

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• Great at back-and-forth dialogue with the caller
• Integration with existing contact center plattforms
• Using the same flow for voice and chat seamlessly
• Very customizable

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• 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

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• Some of the features are still in beta phase.
• The languages available are still limited.
• The advanced features may not be accessible to new users.

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• No-code apps could be improved
• Online docs can be messy
• Steep learning curve

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Users benefit from Dialogflow's best User Interface and seamless User Experience. It's very scalable, and there are a lot of customization options to make it even more so. Dialogflow makes it simple to deploy, manage, and maintain chatbots. Artificial Intelligence algorithms make chatbots interactive, making it easier for users and chatbots to communicate and understand each other. Overall, it's a good option for those with little programming experience who want to learn Natural Language Processing.

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.

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Dialogflow is a wonderful tool that is helping to design customized chatbots. I personally recommend if you wanna know how it's works give it a try with a free APIs call, you'll love this tool

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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

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There is always use cases for both. We still use the other plattforms but each has its own strengths and weaknesses. We, as a contact center implementation partner always use multiple solutions both for ourselves but also for our customers to meet their needs. In the cases we choose Google Cloud Dialogflow is when we need to be able to handle specific follow-up questions from the customer. And for more complicated issues we also use a combination of this and other 3rd party plattforms. We always meet the need of our customers and as specialist and consultants we give expert advice on how to use all these different solutions in the best way possible.

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• As a partner to multiple contact center plattforms we have always been able to offer Google Cloud Dialogflow along with them because it integrates well with all.
• Many businesses want to implement smart ways to have call deflection. Bots that can handle full dialogues with the customer is very intriguing to them because it can fulfill customers requests without using up as many resources

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• Cost Savings & Efficiency
• Increased Conversion Rates
• Improved Customer Satisfaction
• Operational ROI

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