Azure AI Bot Service vs. Rasa

Azure AI Bot service works well if you need to support multiple channels like MS Teams, Slack, Facebook Messenger, etc., using the same code base. It also provides built-in connectors to LUIS and Azure OpenAI for natural language understanding and intelligence. Scenarios such as a low-cost chatbot without the need for LUIS or AI are the ones where Azure AI bot service might not be most appropriate.

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

• Audience Engagement & Support Chatbots for Viewer Bots can answer FAQs about show timings, cast details, subscription plans Etc.
• Interactive Experiences: Bots integrated into apps or behind-the-scenes content to boost viewer interaction.
• Content Discovery & Personalization Bots can guide users to discover new shows or movies based on preferences using natural language queries.
• Social Media & Campaign Automation Bots can automate responses to fan comments, promote upcoming releases, and manage contests or giveaways across platforms like Twitter, Facebook, and Instagram.

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

• They have simplified the coding for the bot in Azure, but it would help if the coding was further simplified so that non-IT can operate [and] create it easily.

• 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

Azure Bot Service provides an integrated environment for bot development. Microsoft Azure Cloud is fully compatible and its security features. It's more important to pay attention to the logic of business than the specifics of each messenger. We don't have any issues using the bot framework because the implementation is excellent.

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

They provide instructional meetings, manuals, and backing staff, in addition to other things. Bugs were handily spotted and fixed, which is one of my annoyances about issues.

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

Microsoft Bot Framework is much better and well more established without a lot of proprietary software/coding language. Lex is very limited with integration with standard hardware and network configurations. Lex has performance issues and was too slow to meet near real-time collaboration requirements. Bot Framework complements many other Microsoft communication products and this was key to implementing without a lot of new training required.

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

• Interfaces with the SQL data set, tracks down plans/replies, and tests them with the SDK.
• Utilizing the system is made conceivable by the Bot Dev gateway. We can interface our bot in excess of ten channels, including Twilio Facebook, Twilio, Twilio, and Slack, and that's only the tip of the iceberg.
• It is expensive.

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

Incentivized