Connect GPT-trainer and ifactfind to Build Intelligent Automations

Choose a Trigger

GPT-trainer

When this happens...

Choose an Action

ifactfind

Automatically do this!

Enable Integrations or automations with these events of GPT-trainer and ifactfind

Enable Integrations or automations with these events of GPT-trainer and ifactfind

Actions

Create QA Source

Create QA Source

Create a QA source for a chatbot specified by chatbot uuid

Create URL Source

Create URL Source

Creates a URL source for a chatbot specified by chatbot uuid.

Create Source Tag

Create Source Tag

Create a source tag for a chatbot.

Request a new Action for GPT-trainer

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Frequently Asked Questions

How do I start an integration between GPT-trainer and ifactfind?

To start, connect both your GPT-trainer and ifactfind accounts to viaSocket. Once connected, you can set up a workflow where an event in GPT-trainer triggers actions in ifactfind (or vice versa).

Can we customize how data from GPT-trainer is recorded in ifactfind?

Absolutely. You can customize how GPT-trainer data is recorded in ifactfind. This includes choosing which data fields go into which fields of ifactfind, setting up custom formats, and filtering out unwanted information.

How often does the data sync between GPT-trainer and ifactfind?

The data sync between GPT-trainer and ifactfind typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.

Can I filter or transform data before sending it from GPT-trainer to ifactfind?

Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.

Is it possible to add conditions to the integration between GPT-trainer and ifactfind?

Yes, you can set conditional logic to control the flow of data between GPT-trainer and ifactfind. 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.

GPT-trainer

About 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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ifactfind

About ifactfind

iFactFind is the new standard in client data collection and management. The digital online fact find for financial advisers and their clients.

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