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Dialogflow is a platform that originally emerged from API.AI and can be used for natural dialogue-oriented communication with users. Today, this platform is an NLP service that is part of the Google Cloud Platform. The platform offers its users a complete development suite with code editor, library and many tools. This makes it easier to create a conversational interface. [1]

 

Advantages of Dialogflow

Dialogflow currently offers developers 33 prebuilt agents. These pre-builts can be further adapted and specified to the specific use case as desired. Since these agents already have many intents set up, this saves time in chatbot development.

Each of these 33 prefabricated agents and, of course, the agents you create yourself can be easily connected to various channels such as Google Assistant, Amazon Alexa or Facebook Messenger via one-click integration. In addition, Dialogflow offers 15 different languages (as of June 2019).

In addition, users can use a function to import and export agents and thus save a lot of time.

Similarly, data from external services can be integrated into a chatbot through fulfilments. Fulfillments give developers the opportunity to link public or private APIs or other services to the chatbot to add even more functionality.

Moreover, the NLP service is not only designed for one programming language, but offers numerous SDKs that make the use of other programming languages possible. Furthermore, the graphical user interface is very clear and user-friendly compared to other providers. This makes it possible to get started with Dialogflow quickly. [2]

Limits of Dialogflow

If you want to connect an agent created on Dialogflow to WhatsApp, the service does not offer any integration for this. An adjustment in the own backend system is required to ensure the connection to WhatsApp and to bring data to the required format.

When using your own backend, there are a few things you need to keep in mind:

For example, Dialogflow expects to receive a response within 5 seconds. Otherwise, a timeout occurs. In addition, the system only saves the contexts in a user session for 10 - 20 minutes. To solve this "problem", it is not enough to save the user ID, but any contexts related to this user ID must be saved temporarily.

The platform inherently offers the possibility to view the analytics of a chatbot in order to evaluate activities and optimisation potentials of chatbots. However, the analytical evaluation is very rudimentary and is offered in greater detail by other tools/services. These can, for example, track the user's conversation history or create specific funnels (filters). If a chatbot gets stuck, many users ask to speak to a real person (human handover). Dialogflow does not offer an option for integration in a chatbot. Therefore, it is also necessary to adapt the backend here.

Despite the limitations of Dialogflow, the Google Service is considered Best Practice by BOTfriends and other Chatbot developers.

Costs

The fees to be paid monthly depend on the version used and the pricing model selected. Other factors are the number of requests, the total duration of the audio material processed and the total duration of the telephone calls. This pricing model is an advantage in contrast to other providers, as one does not have to pay a flat monthly/yearly price, but the costs are made up of the resources and requests used. [3]

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Sources

[1] https://www.bigdata-insider.de
[2] https://www.dialogflow.com
[3] https://cloud.google.com