Connect Speechmatics and Neo4j to Build Intelligent Automations
Enable Integrations or automations with these events of Speechmatics and Neo4j
Enable Integrations or automations with these events of Speechmatics and Neo4j
Triggers
New Job
Triggered when a new job is created in speechmatics.
Actions
Get Job
Get job details, including progress and any error reports.
Create Job
Create a new job.
Get the usage statistics
Get the usage statistics of speechmatics.
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Frequently Asked Questions
To start, connect both your Speechmatics and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Speechmatics triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Speechmatics data is recorded in Neo4j. This includes choosing which data fields go into which fields of Neo4j, setting up custom formats, and filtering out unwanted information.
The data sync between Speechmatics and Neo4j 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 Speechmatics and Neo4j. 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 Speechmatics
Speechmatics is a leading provider of automatic speech recognition technology, offering advanced solutions for converting spoken language into text. Their platform is designed to support a wide range of languages and dialects, making it ideal for businesses and developers looking to integrate speech-to-text capabilities into their applications. With a focus on accuracy and speed, Speechmatics serves industries such as media, telecommunications, and customer support, providing tools that enhance accessibility and improve communication.
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About Neo4j
Neo4j is a leading graph database platform that enables organizations to unlock the value of connections, influences, and relationships in data. It is designed to handle highly connected data and is used for a variety of applications such as fraud detection, real-time recommendations, and network management.
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