Connect AiSensy and kazm to Build Intelligent Automations

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AiSensy

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kazm

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Enable Integrations or automations with these events of AiSensy and kazm

Enable Integrations or automations with these events of AiSensy and kazm

Actions

Send Template Message

Send Template Message

Send a templated campaign message to a recipient phone number, optionally including template parameters, tags, custom attributes, location, and campaign metadata.

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Frequently Asked Questions

How do I start an integration between AiSensy and kazm?

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

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

Absolutely. You can customize how AiSensy 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 AiSensy and kazm?

The data sync between AiSensy 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 AiSensy 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 AiSensy and kazm?

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

AiSensy

About AiSensy

AiSensy is a platform that leverages artificial intelligence to enhance customer communication and engagement. It provides tools for businesses to automate and optimize their messaging strategies, ensuring efficient and effective interactions with their audience.

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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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