How to connect Google Vertex AI and RingCentral
Create a New Scenario to Connect Google Vertex AI and RingCentral
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Vertex AI, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Vertex AI or RingCentral will be your first step. To do this, click "Choose an app," find Google Vertex AI or RingCentral, and select the appropriate trigger to start the scenario.

Add the Google Vertex AI Node
Select the Google Vertex AI node from the app selection panel on the right.

Google Vertex AI
Configure the Google Vertex AI
Click on the Google Vertex AI node to configure it. You can modify the Google Vertex AI URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the RingCentral Node
Next, click the plus (+) icon on the Google Vertex AI node, select RingCentral from the list of available apps, and choose the action you need from the list of nodes within RingCentral.

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Authenticate RingCentral
Now, click the RingCentral node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your RingCentral settings. Authentication allows you to use RingCentral through Latenode.
Configure the Google Vertex AI and RingCentral Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Vertex AI and RingCentral Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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AI Anthropic Claude 3
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RingCentral
Trigger on Webhook
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Google Vertex AI
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Webhook response

Save and Activate the Scenario
After configuring Google Vertex AI, RingCentral, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Google Vertex AI and RingCentral integration works as expected. Depending on your setup, data should flow between Google Vertex AI and RingCentral (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Vertex AI and RingCentral
RingCentral + Google Vertex AI + Salesforce: When a new call recording is available in RingCentral, the audio file is analyzed by Google Vertex AI using the Gemini model to identify relevant keywords. Based on the analysis, the lead score in Salesforce is updated.
RingCentral + Google Vertex AI + Zendesk: Upon completion of a RingCentral call, the recording is sent to Google Vertex AI for summarization using the Gemini model. Based on the summary, a new Zendesk ticket is created, including relevant tags extracted from the call summary.
Google Vertex AI and RingCentral integration alternatives
About Google Vertex AI
Use Vertex AI in Latenode to build AI-powered automation. Quickly integrate machine learning models for tasks like sentiment analysis or image recognition. Automate data enrichment or content moderation workflows without complex coding. Latenode’s visual editor makes it easier to chain AI tasks and scale them reliably, paying only for the execution time of each flow.
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About RingCentral
Integrate RingCentral with Latenode to automate call logging, SMS alerts, and contact management. Trigger workflows based on call events, automatically updating records in other apps like CRMs or support tools. Use Latenode's visual editor and scripting nodes for customized call handling and data synchronization.
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FAQ Google Vertex AI and RingCentral
How can I connect my Google Vertex AI account to RingCentral using Latenode?
To connect your Google Vertex AI account to RingCentral on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Vertex AI and click on "Connect".
- Authenticate your Google Vertex AI and RingCentral accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze call transcripts with AI and send summaries?
Yes, you can! Latenode lets you trigger Vertex AI analysis on RingCentral call recordings and send summarized insights, boosting agent productivity and response quality.
What types of tasks can I perform by integrating Google Vertex AI with RingCentral?
Integrating Google Vertex AI with RingCentral allows you to perform various tasks, including:
- Automatically summarize customer support calls using Vertex AI.
- Route calls based on sentiment analysis from Vertex AI.
- Generate personalized responses for RingCentral messages.
- Create AI-powered call reports with transcript analysis.
- Extract key data points from calls using Vertex AI models.
Can I use custom models in Vertex AI with RingCentral data?
Yes, Latenode allows seamless integration of custom Vertex AI models for tailored RingCentral data processing and insights.
Are there any limitations to the Google Vertex AI and RingCentral integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large volumes of call data processing may incur Vertex AI costs.
- Real-time sentiment analysis is subject to processing delays.
- Custom model deployment in Vertex AI requires advanced knowledge.