Connect Aimfox and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Aimfox
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Aimfox and TimescaleDB
Enable Integrations or automations with these events of Aimfox and TimescaleDB
Actions
Add Profile To Campaign
Adds someone to an AimFox campaign using their profile link.
Remove Profile from Campaign
Removes a profile from a campaign so they stop receiving campaign messages.
Update Campaign
Updates an existing campaign in AimFox.
Search Campaign
Retrieve campaign details using the campaign's ID.
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Frequently Asked Questions
To start, connect both your Aimfox and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Aimfox triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Aimfox 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 Aimfox 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 Aimfox 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 Aimfox
Reach a wider audience with a powerful LinkedIn automation tool. AimFox is made for sales, marketing, HR teams and everyone else who wishes to make automated LinkedIn connections.
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.
Learn More
