IntegrationsProsa AITimescaleDB
Prosa AI + TimescaleDB

Connect Prosa AI and TimescaleDB to Build Intelligent Automations

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Frequently Asked Questions

How do I start an integration between Prosa AI and TimescaleDB?

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

Can we customize how data from Prosa AI is recorded in TimescaleDB?

Absolutely. You can customize how Prosa 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.

How often does the data sync between Prosa AI and TimescaleDB?

The data sync between Prosa AI and TimescaleDB 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 Prosa AI to TimescaleDB?

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 Prosa AI and TimescaleDB?

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

Prosa AI

About Prosa AI

Prosa AI is a cutting-edge platform that leverages artificial intelligence to provide innovative solutions for various industries. It focuses on enhancing productivity, automating tasks, and delivering intelligent insights to help businesses make data-driven decisions. With its advanced AI capabilities, Prosa AI aims to transform the way organizations operate, offering tools that streamline processes and improve efficiency.

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TimescaleDB

About 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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