Dialog flow

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Dialog flow is a platform originally developed from API.AI, which can be used for natural dialog-oriented communication with users. Today this platform is an NLP service, which 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 ready-made agents. These prebuilts can be further adapted and specified to the specific use case as required. Since these agents have a lot of Intents this saves time in chatbot development.

Each of these 33 ready-made agents and of course the agents you have created yourself can easily be 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).

Users can also use a feature to import and export agents, saving a lot of time.

Data from external services can also be integrated into a chat bot by means of fulfillments. Fulfillments give developers the ability to link public or private APIs or other services to the chatbot to add even more functionality.

The NLP service is not only designed for one programming language, but also offers numerous SDKs that enable the use of other programming languages. Furthermore, the graphical user interface is very clear and user-friendly compared to other providers. This allows a quick entry into Dialogflow. [2]

Limits of Dialogflow

If you want to connect an agent created on Dialogflow to WhatsApp, the service does not offer integration for it. An adaptation in your 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:

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

The platform offers an inherent possibility to view the analytics of a chatbot in order to evaluate activities and optimization potentials of chatbots. However, the analytical evaluation is very rudimentary and is offered by other tools/services in more detail. They can, for example, track users' call histories or create specific funnels (filters). If a chat bot doesn't know any more, then many users demand to talk to a real person (human handover). Dialogflow does not offer a possibility for integration in a chatbot. Therefore, it is also necessary to adjust the backend here.

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


The fees to be paid monthly depend on the version used and the price model selected. Other factors include the number of requests, the total duration of the audio material processed, and the total duration of telephone calls. This pricing model is an advantage compared to other providers, since you do not have to pay a flat monthly/annual price, but the costs consist of the resources and requests used. [3]

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