Connect Maestra and Nyckel to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Maestra
Choose an Action
Automatically do this!
Import to Classifier
Classify
Center Detect
Train Classifier
Enable Integrations or automations with these events of Maestra and Nyckel
Enable Integrations or automations with these events of Maestra and Nyckel
Actions
Create Channel
Create a new channel within a workspace
Upload File
Initiates a new file upload to Maestra.
List Files
Grabs the file list with key id and basic information.
Import to Classifier
Import data to your Nyckel classifier
Classify
Classify image or text data
Center Detect
Detect Objects center in images
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Frequently Asked Questions
To start, connect both your Maestra and Nyckel accounts to viaSocket. Once connected, you can set up a workflow where an event in Maestra triggers actions in Nyckel (or vice versa).
Absolutely. You can customize how Maestra data is recorded in Nyckel. This includes choosing which data fields go into which fields of Nyckel, setting up custom formats, and filtering out unwanted information.
The data sync between Maestra and Nyckel 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 Maestra and Nyckel. 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 Maestra
Maestra is a highly accurate speech to text software. Translate in English, French, Spanish, German and 80+ languages.
Learn MoreAbout Nyckel
Nyckel is a platform that provides machine learning as a service, enabling users to easily integrate AI capabilities into their applications without needing extensive knowledge in data science or machine learning. It offers tools for creating, training, and deploying machine learning models with ease.
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