Connect ChatBotKit and chattermill to Build Intelligent Automations
Enable Integrations or automations with these events of ChatBotKit and chattermill
Enable Integrations or automations with these events of ChatBotKit and chattermill
Actions
Add Dataset Record
Add a new text record to the selected dataset.
Create Conversation
Start a conversation with initial messages; optionally attach a contact, task, space, or bot.
Create dataset
Create a dataset to train or update your chatbot
Fetch DataSet
This action fetch dataset detail.
Delete Dataset
Delete a dataset from ChatBotKit
Create Dataset Record
Add a knowledge record to a dataset so the chatbot can find relevant answers.
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Frequently Asked Questions
To start, connect both your ChatBotKit and chattermill accounts to viaSocket. Once connected, you can set up a workflow where an event in ChatBotKit triggers actions in chattermill (or vice versa).
Absolutely. You can customize how ChatBotKit data is recorded in chattermill. This includes choosing which data fields go into which fields of chattermill, setting up custom formats, and filtering out unwanted information.
The data sync between ChatBotKit and chattermill typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between ChatBotKit and chattermill. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About ChatBotKit
ChatBotKit is a comprehensive platform designed to help businesses and developers create, manage, and deploy chatbots with ease. It offers a range of tools and features to build intelligent conversational agents that can enhance customer interaction and automate support tasks.
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About chattermill
Chattermill is a powerful experience analytics platform. We use the latest in deep learning to extract relevant insight from customer feedback.
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