Connect Frame.io and kazm to Build Intelligent Automations

Choose a Trigger

Frame.io

When this happens...

Choose an Action

kazm

Automatically do this!

Enable Integrations or automations with these events of Frame.io and kazm

Enable Integrations or automations with these events of Frame.io and kazm

Actions

Get a user

Get a user

Get the current authenticated User

Request a new Action for Frame.io

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Frequently Asked Questions

How do I start an integration between Frame.io and kazm?

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

Can we customize how data from Frame.io is recorded in kazm?

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

How often does the data sync between Frame.io and kazm?

The data sync between Frame.io and kazm 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 Frame.io to kazm?

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 Frame.io and kazm?

Yes, you can set conditional logic to control the flow of data between Frame.io and kazm. 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.

Frame.io

About Frame.io

Frame.io is a video review and collaboration platform designed to streamline the post-production process. It allows teams to upload, review, and share video content seamlessly, making it easier to gather feedback and make revisions.

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kazm

About kazm

Kazm is the data-driven CRM for Web3. We plug into every community touchpoint to create unified customer profiles, then apply machine learning and other methods to label, segment, and help personalize experiences for users.

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