Azure AI Bot Service vs. Google Cloud Dataflow vs. Google Cloud Dialogflow

From personal experience, I can recommend the Azure AI service to reduce the burden on your customer service team, as we did. We created a customer service bot and automated our Frequently Asked Questions section, as well as an interactive platform for registering other complaints that the bot cannot handle, which are later addressed by our customer service team.

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It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.

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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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• Assembles conversational AI encounters for our clients
• Adds regular language, discourse, and vision
• Sends to well known channels with insignificant code changes

• Streaming, Real time work load
• Batch processing
• Auto scaling
• flexible pricing

• 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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• Limited Natural Language Understanding, especially in entertainment contexts where slang, humor
• Works only with Azure platform only not compatible with other platforms
• Complex customization as per organization need

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• More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
• I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
• Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.

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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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Azure AI Bot Service comes with an Integrated Development Environment and Bot Framework SDK that simplifies development. Having a single codebase for multiple channels, such as MS Teams, Facebook, and Slack, makes it easier to target multiple platforms without requiring redevelopment. LUIS and OpenAI can be easily integrated with your Chatbot to provide a seammless chat bot expereince to customers.

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It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.

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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.

Microsoft's customer service staff is friendly and extremely knowledgeable. They are always available to assist you with bot framework-related issues that you may be experiencing. They provide training sessions, manuals, and support personnel, among other things. Bugs were easily spotted and patched, which was one of my pet peeves about patching.

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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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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.

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Google Cloud Dataproc Cloud Datafusion

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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• Azure Bot will only deliver according to the granularity of the FAQ it is feed. Otherwise, it has been a cost optimization tool for our organization.

• cost saving from managing our own data center for ETL servers
• consumption based pricing
• with auto scaling feature, we were able to expand components to support work load

• 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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