Connect Frill and GPT-trainer to Build Intelligent Automations
Choose a Trigger
When this happens...
New Idea
Choose an Action
Automatically do this!
Create QA Source
Create URL Source
Create Source Tag
Ready to use Frill and GPT-trainer automations
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- text is aiReplytoCommentSend a Private ReplyAdd Step
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Idea
Triggers when a new Idea is created.
Do thisActions
Action is the task that follows automatically within your Frill integrations.
Update Idea
Update an existing Idea.
List All Ideas
Fetch all ideas from your Frill account and return them as a single list.
Create QA Source
Create a QA source for a chatbot specified by chatbot uuid
Create URL Source
Creates a URL source for a chatbot specified by chatbot uuid.
Create Source Tag
Create a source tag for a chatbot.
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Frequently Asked Questions
To start, connect both your Frill and GPT-trainer accounts to viaSocket. Once connected, you can set up a workflow where an event in Frill triggers actions in GPT-trainer (or vice versa).
Absolutely. You can customize how Frill data is recorded in GPT-trainer. This includes choosing which data fields go into which fields of GPT-trainer, setting up custom formats, and filtering out unwanted information.
The data sync between Frill and GPT-trainer 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 Frill and GPT-trainer. 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 Frill
Frill lets you collect customer feedback, communicate a public roadmap and announce new features.
Learn MoreAbout GPT-trainer
GPT Trainer is a platform designed to enhance and customize the training of AI models, particularly those based on the GPT architecture. It provides tools and resources for developers and researchers to fine-tune and optimize AI models for specific tasks and applications.
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