Connect Tavus and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Conversation
Updated Conversation
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Tavus and TimescaleDB
Enable Integrations or automations with these events of Tavus and TimescaleDB
Triggers
New Conversation
Trigger when new conversation is created.
Updated Conversation
Trigger when an existing conversation is updated.
Actions
Generate Speech
Generate Speech.
Create Video
Creates a Video.
Update Video
Update an Existing video.
Delete Video
Delete an Existing Video.
Update Replica
Update an Existing replica.
Create Conversation
Creates a Conversation.
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Frequently Asked Questions
To start, connect both your Tavus and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Tavus triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Tavus 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 Tavus 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 Tavus 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 Tavus
Tavus is an innovative platform that leverages artificial intelligence to create personalized video content at scale. It allows businesses to engage with their audience through customized video messages, enhancing customer interaction and marketing efforts.
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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