AI-Powered Entity Resolution

Entity resolution, also known as entity linkage, record matching, or identity resolution, is a data management technique that identifies and matches records across multiple data sources to create a golden record of key business entities.

Entity resolution helps organizations overcome challenges with duplicate data, data integration, data quality, and regulatory compliance by reconciling records across and within datasets. For example, customer entity resolution identifies, matches, and unifies records that refer to the same customer across systems to create a holistic 360-degree view.

Yes, Tamr’s AI-native approach and scalable SaaS architecture enable the platform to handle large-scale matching as the amount of data and number of systems continue to grow.

Tamr uses advanced AI incorporating probabilistic methods to automate every step of the matching process, from initial scanning and smart comparison to labeling, scoring, and intelligent ranking. Further, LLM-based AI agents surface edge cases for curator review and suggest edits to golden records based on change events (e.g., records created, updated, etc). As a result, Tamr eliminates duplication, connects records, and delivers accurate, scalable, and explainable results for customer entities.

AI-based entity resolution combines highly tuned AI/ML models and automated workflows to make the matching process faster and more efficient. And because these models are always learning and adapting, they can scale and improve as the data grows and evolves. In contrast, traditional approaches rely on rules, which are difficult to scale and expensive to maintain.