Connect EKM and Roboflow to Build Intelligent Automations
Choose a Trigger
When this happens...
New Customer
New Order
New Product
Choose an Action
Automatically do this!
Compare Image With Text
Create Classification Prediction
Run Instance Segmentation
Detect Objects In Image
Ready to use EKM and Roboflow automations
Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Customer
Runs when a new customer is created.
New Order
Trigger when new order is created in your account.
New Product
Trigger when new product is created in your account.
Do thisActions
Action is the task that follows automatically within your EKM integrations.
Create Customer
Add a new customer to the EKM account.
Create Product
Add a new Product to the EKM account.
Update Product
Update the details of an existing product.
Compare Image With Text
Measure image-text similarity
Create Classification Prediction
Predict classes for an image
Run Instance Segmentation
Create masks and labels
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Frequently Asked Questions
To start, connect both your EKM and Roboflow accounts to viaSocket. Once connected, you can set up a workflow where an event in EKM triggers actions in Roboflow (or vice versa).
Absolutely. You can customize how EKM 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 EKM 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 EKM 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 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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