Connect kazm and Captions to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for kazm
Choose an Action
Automatically do this!
Submit a video translation request
Enable Integrations or automations with these events of kazm and Captions
Enable Integrations or automations with these events of kazm and Captions
Actions
Submit a video translation request
Translates a video from one language to another
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Automate Instagram Comment Replies with Smart DMs
- text is aiReplytoCommentSend a Private ReplyAdd Step
- Is guide commentReplytoComment GuideSend a Private Reply GuideAdd Step
- multi keyword matchcmntdqJzLMYSReplytoCommentMultiKeywordAdd Step
Automate Expense Report Approvals via Slack & QuickBooks
- Send for Approval acceptCreate Entry of expensesAdd Step
- Send for rejectMail for RejectionAdd Step
Automate Instagram DM Replies, Reactions, and Team Alerts
- has responseSendDMMessageAdd Step
- Notify teamSendMailAdd Step
- has reactSendDMMessage ReactAdd Step
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Frequently Asked Questions
To start, connect both your kazm and Captions accounts to viaSocket. Once connected, you can set up a workflow where an event in kazm triggers actions in Captions (or vice versa).
Absolutely. You can customize how kazm data is recorded in Captions. This includes choosing which data fields go into which fields of Captions, setting up custom formats, and filtering out unwanted information.
The data sync between kazm and Captions 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 kazm and Captions. 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 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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