Oracle Digital Assistant vs. Rasa

As mentioned in pros and cons, it depends on the use cases. Most of the normal chatbot use cases can be handled by ODA. If you want to build a chatbot with menu style or conversation style (it is not straight forward but it can be done), ODA can be perfect. If use cases are to also include AI with emotional intelligence to make conversational more interactive and also be able to detect through AI engine automatically what a person would like to do or perform or ask, then ODA may not fit for it. If you also are looking for virtual agents through tighter IVR integration then ODA may not be right. There are a few limitations around the number of words in the text to voice feature.

Incentivized

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

• Developing new and custom skills for chatbots.
• Developing conversational style skills for chatbots.
• Menu style skills for chatbots.
• Lots of skills those are available out of the box.

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

• Our clients have reported that upon integration with Siri for voice commands, the results are pretty obvious (basic) Nd the conversation is unnatural.
• The languages it supports are very limited.

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

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

I selected Oracle Digital Assistant against all other digital assistant platforms as this platform works like a charm with any Oracle application. It integrates well with Oracle Integration Cloud. The new beta version has an inbuilt conversation builder which can be used to build conversation without the YAML code.

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

• We've had a good integration with our existing databases and applications.
• Our customers are crypto traders and we've had more engagement with them, which means a lot to us.

Incentivized

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

Incentivized