Connect Cradl AI and RunPod to Build Intelligent Automations
Choose a Trigger
When this happens...
New Document
New Dataset
Choose an Action
Automatically do this!
Create a Container Registry
Create a Template
Find a Container Registry Auth
Delete a Container Registry Auth
Delete a Template
Create Registry Authentication
Ready to use Cradl AI and RunPod automations
Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Document
Triggers when a new document is created.
New Dataset
Triggers when a new dataset is created.
Do thisActions
Action is the task that follows automatically within your Cradl AI integrations.
List Documents
Get all existing documents.
Create Dataset
Create a new dataset.
Create Document
Create a new document.
List users
Get all existing users.
List Agent
List all Agent in Cradl AI.
Create agent
Create a new agent.
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Frequently Asked Questions
To start, connect both your Cradl AI and RunPod accounts to viaSocket. Once connected, you can set up a workflow where an event in Cradl AI triggers actions in RunPod (or vice versa).
Absolutely. You can customize how Cradl AI data is recorded in RunPod. This includes choosing which data fields go into which fields of RunPod, setting up custom formats, and filtering out unwanted information.
The data sync between Cradl AI and RunPod 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 Cradl AI and RunPod. 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 Cradl AI
Cradl AI is a no-code tool for automating document workflows using customized AI - fully automated or with human-in-the-loop validation.
Learn MoreAbout RunPod
RunPod is a platform that provides cloud-based computing resources for running and managing machine learning models, data processing tasks, and other computational workloads. It offers scalable and efficient solutions for developers and data scientists to deploy and execute their projects seamlessly.
Learn More
