Connect Ringg AI and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
Conversation Ends
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Ringg AI and TimescaleDB
Enable Integrations or automations with these events of Ringg AI and TimescaleDB
Triggers
Conversation Ends
Runs when conversation ends
Actions
List Assistants
Show all assistants in account
Get call details
Get details for a specific call, including optional AI analysis
Get Workspace Numbers
Retrieve the phone numbers available in this workspace.
Place an outbound call
Place an outbound call to a contact or phone number
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Frequently Asked Questions
To start, connect both your Ringg AI and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Ringg AI triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Ringg AI 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 Ringg AI 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 Ringg AI 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 Ringg AI
Ringg AI offers advanced AI-driven solutions to enhance communication and customer interaction. With cutting-edge technology, it streamlines processes and improves efficiency for businesses.
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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