Integrations

Supercharge Interzoid with Relevance AI

Interzoid offers powerful data matching and analysis capabilities through AI/ML-based data engineering. By integrating with Relevance AI, you can elevate your data operations with intelligent insights and automated decision-making.

Give your AI Agents Interzoid Superpowers

Interzoid provides advanced data matching and analysis capabilities, while Relevance AI empowers these processes with intelligent AI Agents that can analyze and act on data insights.

Intelligent Data Orchestration

The agent gains the ability to seamlessly harmonize and standardize data across multiple sources in real-time.

Global Entity Recognition

Empowers the agent to accurately identify and match company names and addresses across international formats and languages.

Precision Matching Intelligence

Equips the agent with advanced fuzzy matching capabilities for superior entity resolution and deduplication.

Tools

Equip AI Agents with the Interzoid Tools they need

Relevance AI seamlessly integrates with Interzoid to enhance your data matching workflows.

Interzoid - Get Organization Match Score
Compares two organization names and generates a similarity match score using AI and ML algorithms to determine how closely they match, helping identify potential duplicate or related entities.
Interzoid - Generate Match Report
Creates comprehensive matching reports by analyzing data from various database sources using specialized ML algorithms for comparing companies, individual names, or addresses across different data formats and storage systems.
Interzoid - Get Full Name Match Score
Evaluates the similarity between two full person names using advanced matching algorithms to determine how closely they match, useful for identifying potential duplicate records or variations of the same person's name.
Name
Interzoid API Call
Description
Make an authorized request to a Interzoid API
Parameters
["OAuth authentication", "Multiple HTTP methods (GET, POST, PUT, DELETE, PATCH)", "Custom headers support", "Request body configuration", "Response handling with status codes"]
Use Case
A data quality team uses Interzoid API calls to validate and standardize customer contact information in real-time, ensuring accurate address matching and reducing duplicate entries in their CRM system. The integration automatically cleanses incoming customer data and flags potential inconsistencies for review.

Security & Reliability

The Interzoid & Relevance AI integration leverages advanced AI/ML-based data engineering to provide robust data matching and analysis capabilities. This integration allows you to seamlessly compare and match organization names, generate detailed match reports across diverse data sources, and perform full name matching with similarity scoring.

Utilizing secure OAuth authentication, the integration ensures that only authorized workflows can access your data. Relevance AI manages API operations (such as POST requests for matching and report generation) in the background, alleviating concerns about errors, formatting, or limits.

Built-in validation and type conversion guarantee that your workflows operate smoothly, even when dealing with varying data formats. To get started, ensure you have the necessary accounts, credentials, and system requirements in place, and follow the installation and configuration steps outlined in the guide.

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.

Get Started

Best Practices for Non-Technical Users

To get the most out of the Interzoid + Relevance AI integration without writing code:
  • Start with clean data: Ensure organization names and full names are standardized and free of special characters.
  • Utilize match reports: Generate comprehensive match reports to analyze data across various sources effectively.
  • Configure OAuth correctly: Double-check your OAuth credentials and permissions to ensure seamless integration.
  • Test with sample data: Validate your matching logic using a small dataset before applying it to larger datasets.
  • Monitor performance: Implement caching for frequently matched results and optimize database queries for better efficiency.