Connect Clap.video and Scale to Build Intelligent Automations
Enable Integrations or automations with these events of Clap.video and Scale
Enable Integrations or automations with these events of Clap.video and Scale
Actions
Create User
Creates a User.
Get Tasks
Retrieve all the tasks for given project.
Create Batch
Create a new batch in a project to group related tasks for processing.
Pause Batch
Pause processing of an in-progress Batch. Only unstarted tasks will be paused.
Resume Batch
Resume processing of a paused Batch. Previously paused tasks will be resumed.
Finalize a batch
Mark a Scale batch as complete so no new tasks can be added.
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Frequently Asked Questions
To start, connect both your Clap.video and Scale accounts to viaSocket. Once connected, you can set up a workflow where an event in Clap.video triggers actions in Scale (or vice versa).
Absolutely. You can customize how Clap.video data is recorded in Scale. This includes choosing which data fields go into which fields of Scale, setting up custom formats, and filtering out unwanted information.
The data sync between Clap.video and Scale 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 Clap.video and Scale. 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 Clap.video
Video Clap is a dynamic platform designed to enhance your video creation and sharing experience. It offers tools for editing, collaborating, and distributing video content seamlessly, making it ideal for content creators, marketers, and businesses looking to engage their audience through compelling video narratives.
Learn MoreAbout Scale
Scale provides advanced AI-driven solutions for data labeling and annotation, helping businesses streamline their machine learning workflows.
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