Metadata in Relevance AI helps you extract and organize key information from conversations. These structured data points act as smart labels that make your conversations:
Searchable - Find exactly what you need
Filterable - Sort conversations by specific criteria
Analyzable - Gain insights across multiple interactions
For example, you might add metadata like “customer_segment,” “sentiment_score,” or “product_category” to categorize and later analyze patterns in your conversations.
Adding metadata allows you to enrich your data with additional context:
Navigate to the Build tab located at the top of your screen, adjacent to Tasks
Click on “Metadata” in the left sidebar navigation menu
Choose your extraction method (Agent-decided or Rule-based)
Define the appropriate data format and configure your settings
Click the “Save” button to apply your changes
Consider what information would be most valuable for future analysis when deciding which metadata to add. Focus on data points that will help you segment, filter, or understand patterns in your agent interactions.
Find the metadata field you want to remove in the Metadata page
Click the delete (trash) icon next to the field
Deleting metadata is permanent and will remove this information from all associated conversations or tasks. Make sure you no longer need this data before proceeding.
Predefined list where only one value can be selected
Example: Lead status: [“New”, “Qualified”, “Opportunity”, “Customer”]
Multiple Option
Predefined list where multiple values can be selected
Example: Products of interest: [“Product A”, “Product B”, “Product C”]
Choose the appropriate data format based on how you plan to use the metadata later. For example, if you want to calculate averages or totals, use the number format. If you need to filter by specific categories, single or multiple option formats work best.
Be consistent: Use standardized naming conventions and values for your metadata
Start small: Begin with a few key metadata fields and expand as needed
Review regularly: Periodically assess which metadata fields are providing value
Document your schema: Keep track of what each metadata field represents and how it should be used
By effectively utilizing metadata, you can transform raw conversation data into structured, actionable insights that drive better decision-making and agent performance.