Solving Real-world Entity Resolution at Scale with AI

It’s a well-known fact that entity resolution is hard work. And as data grows larger and increasingly complex, resolving entities across systems and sources becomes exponentially more difficult. 

Several additional factors further complicate the entity resolution challenge, including:

• Volume of potential comparisons 
• Imbalanced classifications
• Absence of ground truth
• Quality and diversity of data
• Need for multi-domain expertise

To address these challenges, Tamr is taking an AI-first approach to entity resolution, enhancing the overall process by autonomously managing data quality, scalability, and adaptive learning. 

Tamr’s AI-driven Approach to Solving Entity Resolution

Tamr employs an AI-driven architecture that embodies a number of specialized AI techniques. These core components include:

• Feature Extraction: The process of transforming raw data into representations that work for machine learning. ‍
• Blocking and Pre-grouping: Intelligent blocking and pre-grouping mechanisms that address the quadratic complexity challenge. Pre-grouping further identifies matching records and aggregates them before the entity resolution process. ‍
• Enrichment: The validation, standardization, and addition of data attributes using high-quality, external information. ‍
• Pairwise Classification and Clustering: The ability to predict whether or not two records represent the same entity, helping to ensure efficient clustering. ‍
• Adaptive Learning and Categorization: Capabilities designed to enhance the ability of Tamr’s AI-driven architecture to continuously improve and organize data effectively. ‍
• Semantic (Embedding) Search: Functionality that improves matching accuracy through a deeper understanding of relationships driven by meaning, intent, and context. 

These capabilities enable Tamr’s AI-driven architecture to deliver greater efficiency, scalability, and adaptability in its entity resolution processes, allowing Tamr to reconcile 500 million records in less than four hours. 

Tamr’s solution is architected in a way to easily incorporate new capabilities as AI continues to evolve in areas such as semantic understanding, adaptive workflows, and autonomous decision-making. Further, by embracing advancements in areas such as model context protocols, improved clustering algorithms, large language models (LLMs), and agentic AI, Tamr remains a well-positioned leader in the field of entity resolution. 

To learn more about how Tamr solves entity resolution using AI, please download our detailed white paper.