How to connect Phantombuster and Google Vertex AI
Create a New Scenario to Connect Phantombuster and Google Vertex AI
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 Phantombuster, triggered by another scenario, or executed manually (for testing purposes). In most cases, Phantombuster or Google Vertex AI will be your first step. To do this, click "Choose an app," find Phantombuster or Google Vertex AI, and select the appropriate trigger to start the scenario.

Add the Phantombuster Node
Select the Phantombuster node from the app selection panel on the right.

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

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Authenticate Google Vertex AI
Now, click the Google Vertex AI node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Vertex AI settings. Authentication allows you to use Google Vertex AI through Latenode.
Configure the Phantombuster and Google Vertex AI 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 Phantombuster and Google Vertex AI 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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Save and Activate the Scenario
After configuring Phantombuster, Google Vertex AI, 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 Phantombuster and Google Vertex AI integration works as expected. Depending on your setup, data should flow between Phantombuster and Google Vertex AI (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Phantombuster and Google Vertex AI
Phantombuster + Google Vertex AI + Slack: Phantombuster monitors competitor websites and triggers the flow when new output is available. Google Vertex AI analyzes the downloaded content, and Slack sends a message to a specified channel with a summary of the analysis.
Phantombuster + Google Vertex AI + Google Sheets: Phantombuster scrapes customer reviews. When the scraping is complete, the output is sent to Google Vertex AI. Vertex AI categorizes the scraped reviews, and the categorized data is added to a Google Sheet.
Phantombuster and Google Vertex AI integration alternatives
About Phantombuster
Automate web data extraction with Phantombuster in Latenode. Scrape social media, websites, and directories to enrich your workflows. Build scalable lead generation or market research processes by integrating Phantombuster's agents directly into Latenode's visual, no-code environment. Use Latenode’s flexible tools for data transformation and routing.
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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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See how Latenode works
FAQ Phantombuster and Google Vertex AI
How can I connect my Phantombuster account to Google Vertex AI using Latenode?
To connect your Phantombuster account to Google Vertex AI on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Phantombuster and click on "Connect".
- Authenticate your Phantombuster and Google Vertex AI accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I enrich scraped LinkedIn profiles with AI-driven insights?
Yes, you can! Latenode enables seamless data transfer for enriched profiles, saving time by combining Phantombuster's scraping with Google Vertex AI's analytics.
What types of tasks can I perform by integrating Phantombuster with Google Vertex AI?
Integrating Phantombuster with Google Vertex AI allows you to perform various tasks, including:
- Automatically generating personalized outreach messages from scraped data.
- Analyzing competitor strategies extracted via Phantombuster with AI models.
- Classifying leads obtained from Phantombuster based on Google Vertex AI analysis.
- Creating dynamic content informed by web scraping and AI-driven insights.
- Building custom AI applications using scraped data processed by Vertex AI.
How do I handle Phantombuster rate limits in Latenode workflows?
Latenode lets you manage rate limits with built-in delay and error handling, ensuring robust and reliable Phantombuster automation.
Are there any limitations to the Phantombuster and Google Vertex AI integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large data transfers between apps may affect workflow execution speed.
- Advanced Google Vertex AI features require appropriate subscription levels.
- Custom JavaScript code might be needed for complex data transformations.