Connect Woovi and Roboflow to Build Intelligent Automations
Enable Integrations or automations with these events of Woovi and Roboflow
Enable Integrations or automations with these events of Woovi and Roboflow
Actions
Create a Charge Refund
Issue a full or partial refund for a charge using its correlation ID.
Create Charge
Create a new Woovi payment charge (PIX or overdue) and return the charge details.
Create a Pix QrCode Static
Generate a static PIX QR code for a payment and return the QR image, BR code, and payment link.
Create a new Customer
Create a new customer in the system.
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 Woovi and Roboflow accounts to viaSocket. Once connected, you can set up a workflow where an event in Woovi triggers actions in Roboflow (or vice versa).
Absolutely. You can customize how Woovi 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 Woovi 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 Woovi 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 Woovi
Woovi makes it easy to create instant payments — including charges, subscriptions, PIX payments, and webhooks. Build faster, simplify workflows, and increase your revenue with seamless Woovi automation in ViaSocket.
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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