Skip to main content
search

Purpose-Built AI & ML

Smart entity resolution from day one.

Traditional entity resolution systems are costly, time-consuming and error-prone, often failing to effectively handle varied edge cases. Without advanced artificial intelligence (AI) and machine learning (ML) capabilities, tasks like cleansing new data sets for ingestion and threshold configuration become cumbersome if not overwhelming.

Senzing® created a purpose-built real time AI for entity resolution. One pivotal breakthrough is our principle based entity resolution which combines common sense and real time learning. Built-in machine-learned domain-specific knowledge allows Senzing to deliver human-smart results out of the box. Then in real time, Senzing is learning, and this learning is instantly integrated into past, present and future decision-making. As a result, Senzing technology delivers higher accuracy and easier deployment than any other entity resolution solution.

The Senzing Purpose-Built AI for
Entity Resolution

 Icon 65 purpose-built ai & ml - Accurate entity resolution on day one that gets smarter over time

 Icon png 82 purpose-built ai & ml - Dramatic time-to-value advantages compared to other solutions

 Icon png 72 purpose-built ai & ml - Lower costs compared to systems without powerful real time AI

Built-In Machine-Learned Domain Knowledge

Our common sense artificial intelligence (AI) uses built-in machine-learned domain knowledge that allows Senzing entity resolution to be smart from day one, even with small data sets. This also ensures the system’s real time learning is not fooled by newly introduced anomalies, such as mismapped fields or other errors.

 Icon png 76 purpose-built ai & ml - Culturally-Aware Name Recognition and Global Address Parsing

The Senzing AI boasts more than ten comparison routines for attributes like phone numbers, dates and SSNs. These highly advanced comparators ensure accurate entity resolution by incorporating culturally aware name recognition – including a pre-trained library with 800 million global names and nicknames developed over decades – and global address parsing with libpostal.

Real Time Machine Learning in the Senzing AI

The Senzing AI uses real time machine learning (ML) to deliver Entity Centric Learning™, support anomaly detection and provide sequence neutral processing.

 Icon png 8 purpose-built ai & ml -Entity Centric Learning

The Senzing AI retains history and attribute variations for each entity as it resolves new records against existing entities, i.e., learning every name, address and phone variation. Over time, based on the accumulated variations, the AI learns nicknames, alternative email addresses, common typographical errors and intentionally fabricated information.

 Icon png 41 purpose-built ai & ml - Statistical Exception (Anomaly) Detection

Senzing entity resolution actively tracks feature statistics in real time as it resolves and relates entities without any data flowing to Senzing (the Senzing API runs on-premises, in the cloud or hybrid, and no data flows to Senzing). Based on the information your system has seen to date, the software keeps detailed statistics about its entity repository, e.g., containing approximately 150 million males, 500 people with the same DOB, and exactly seven people who have lived at 626 Elm Street.

By comparing actual statistics to expected attribute behaviors, Senzing entity resolution helps support anomaly detection such as garbage values, e.g., if the SSN value 121212121 is used by hundreds of entities, the software recognizes this as an exception, since SSNs generally belong to one person.

 Icon png 19 purpose-built ai & ml -Sequence Neutrality: Self-Correcting the Past

Based on what it learns about entities and anomalies, Senzing software continuously evaluates its earlier assertions to determine if they need correction. Sequence neutrality allows the software to self-correct the past in real time, whether it received record A first then B, or vice versa. Without sequence neutrality, entity resolution systems have accuracy drift, with error rates increasing between the periodic reloads required to bring them up to date.

Unlike many AI and ML techniques that must initially be trained using extremely large data sets, Senzing entity resolution’s purpose-built AI is pre-trained, pre-tuned and highly accurate from day one.

SCHEDULE A DEMO

Additional AI and ML Resources:

Entity resolution & machine learning - why you need both
Video
Entity Resolution & Machine Learning – Why You Need Both
Entity resolution buyers guide senzing resource
Core Concepts
Entity Resolution Buyer’s Guide
Entity resolution- insights and implications for ai applications
Blog
Entity Resolution: Insights and Implications for AI Applications
How senzing uses ml for entity resolution - types of machine learning
Video
How Senzing Uses Machine Learning for Entity Resolution
White papers ft. Image
White Papers
Uniquely Senzing: Six Ways Our Entity Resolution Stands Out
Why machine learning needs entity resolution
Video
Why Machine Learning Needs Entity Resolution
Close Menu