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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Customer
Trigger when a new customer created.
Update Customer
Triggers when an existing customer is updated.
Do thisActions
Action is the task that follows automatically within your Urable integrations.
Search Customer
Retrieves the detail of customer based on email address.
List All Items
Retrieves the detail of all items.
Create Customer
Creates a new customer
Update Customer
Updates an existing customer
Compute CLIP Similarity
Compute how closely an image matches a text prompt using Roboflow's CLIP model.
Create Classification Prediction
Generate class predictions for an image.
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Frequently Asked Questions
To start, connect both your Urable and Roboflow accounts to viaSocket. Once connected, you can set up a workflow where an event in Urable triggers actions in Roboflow (or vice versa).
Absolutely. You can customize how Urable data is recorded in Roboflow. This includes choosing which data fields go into which fields of Roboflow, setting up custom formats, and filtering out unwanted information.
The data sync between Urable and Roboflow 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 Urable and Roboflow. 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 Urable
Urable is an application designed for automotive businesses that specialize in detailing, paint protection film, vinyl wrapping, tinting, and ceramic coating. As a cloud-based application with dedicated mobile apps, it allows you to run your business from multiple devices in real-time.
Learn MoreAbout Roboflow
Roboflow is a leading platform for computer vision and AI model development, offering tools to streamline the process of building, training, and deploying machine learning models.
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