Connect Clientary and kazm to Build Intelligent Automations
Enable Integrations or automations with these events of Clientary and kazm
Enable Integrations or automations with these events of Clientary and kazm
Triggers
New Expense
A new expense in Clientary.
New Project
Triggered when a new project is created in Clientary.
New Client
Triggered when a new client is created in Clientary.
New Estimate
Triggered when a new estimate is created in Clientary.
New Invoice
A new invoice is created in Clientary.
New Contracts
An individual Contracts within Clientary.
Actions
Create Client
Create a new client in Clientary.
Create Expense
Create a new expense in Clientary.
Create Task
Create a task inside Clientary.
Create Entry
Create A New Entry.
Create Invoice Payment
Create A New Invoice Payment.
Update Expense
Update an exsiting expense.
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Frequently Asked Questions
To start, connect both your Clientary and kazm accounts to viaSocket. Once connected, you can set up a workflow where an event in Clientary triggers actions in kazm (or vice versa).
Absolutely. You can customize how Clientary 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.
The data sync between Clientary and kazm 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 Clientary 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.
About Clientary
Online invoicing and time tracking for small businesses and freelancers. Clientary helps you get paid quicker and painlessly helps you manage your staff and projects.
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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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