Connect Speechnotes and kazm to Build Intelligent Automations

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Speechnotes

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Choose an Action

kazm

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Enable Integrations or automations with these events of Speechnotes and kazm

Enable Integrations or automations with these events of Speechnotes and kazm

Actions

Transcribe Audio

Transcribe Audio

Convert an audio or video file into a text transcript. Supports language selection, speaker count, and optional partial-range transcription.

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Frequently Asked Questions

How do I start an integration between Speechnotes and kazm?

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

Can we customize how data from Speechnotes is recorded in kazm?

Absolutely. You can customize how Speechnotes 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 Speechnotes and kazm?

The data sync between Speechnotes 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 Speechnotes 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 Speechnotes and kazm?

Yes, you can set conditional logic to control the flow of data between Speechnotes 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.

Speechnotes

About Speechnotes

Automatically transform all audio recordings to text. Most accurate AI transcription (speech to text) engines. Fast, affordable, secure & private.

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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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