Connect Callingly and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Callingly
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Callingly and TimescaleDB
Enable Integrations or automations with these events of Callingly and TimescaleDB
Actions
Create Client
Create a new client in Callingly using the supplied contact details and return the created client's information.
Call Lead
Creates or updates a Lead, calls your Team and puts the first to accept through to the Lead's phone number.
Create Team
Create a team.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Callingly and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Callingly triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Callingly data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between Callingly and TimescaleDB 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 Callingly and TimescaleDB. 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 Callingly
Callingly is a powerful tool designed to help businesses connect with leads instantly by automating the process of making phone calls. It ensures that sales teams can respond to inquiries quickly, improving conversion rates and customer satisfaction.
Learn MoreAbout TimescaleDB
TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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