Zylvie is an advanced data integration and automation platform that streamlines workflows and enhances data accessibility.
Supercharge your data operations with AI Agents that can intelligently process and act on your connected data sources.
Zylvie excels at connecting and transforming data across multiple sources. Relevance AI enhances this by enabling intelligent AI Agents that can analyze, act on, and derive insights from this unified data.
Seamless Data Orchestration
Effortlessly connects and harmonizes data from diverse sources.
Intelligent Workflow Automation
Streamlines complex processes, reducing manual intervention and errors.
Real-time Insight Generation
Delivers immediate analytics to support timely decision-making.
Relevance AI gives you access to Zylvie's data integration and automation capabilities within your AI-powered workflows.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- A Zylvie account
- A Relevance AI account with access to your project settings
- Authorization (connect securely via OAuth—no manual credential storage)
Security & Reliability
The integration uses secure OAuth authentication, ensuring only authorized workflows access your Zylvie data. Relevance AI handles API operations (like GET, POST, PUT, DELETE, PATCH) in the background—so you don't have to worry about errors, formatting, or limits.
Built-in validation and type conversion ensure your workflows run smoothly, even when data formats vary.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
Best Practices for Non-Technical Users
To get the most out of the Zylvie + Relevance AI integration without writing code:
- Use proper authentication: Ensure your OAuth credentials are correctly configured for API access.
- Follow API conventions: Use appropriate HTTP methods (GET, POST, PUT) for different operations.
- Structure requests properly: Include required headers and format request bodies according to API specs.
- Handle responses carefully: Monitor response status codes and handle errors appropriately.
- Optimize API calls: Batch requests when possible and implement proper error handling.
