I keep missing duplicates because names and titles are spelled slightly differently across records.

This feature helps you identify potential duplicate records even when names and titles have minor spelling differences. It reduces missed matches and improves data quality by surfacing likely duplicates for review.

This feature is designed to prevent duplicate records from being overlooked when key fields like names and titles are entered with slight variations. It detects likely duplicates based on similarity rather than requiring exact text matches. When you create, import, or review records, it can highlight entries that look like the same person, company, or item despite small spelling changes. This supports common inconsistencies such as typos, abbreviations, punctuation differences, and alternative word forms in titles. The feature enables more consistent record matching so teams can maintain a cleaner, more reliable dataset. It is especially useful in systems where multiple users contribute data and naming conventions vary. By reducing duplicate fragmentation, it improves reporting accuracy and makes search results and outreach workflows more dependable. It also lowers the time spent manually comparing records and reconciling duplicates. Use it when importing lists, cleaning legacy data, or ensuring new entries do not unintentionally create duplicates.