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Purpose-Built AI & ML

Smart entity resolution from day one.

Ai & ml artificial intelligence and machine learning for entity resolution

Senzing AI is an entity resolution engine that delivers consistent, explainable results with unprecedented speed and precision without the accuracy drift common in other systems. It’s not generative AI, doesn’t use LLMs, and never hallucinates. Senzing learns locally on your data, which remains in your environment and never leaves your control.

Purpose-built for entity resolution, Senzing AI arrives with advanced algorithms that are highly accurate on day one, with no configuration, ramp-up, or entity resolution experts required. Unlike other systems that demand extensive setup before producing trustworthy results, Senzing delivers out-of-the-box accuracy and continues to improve over time.

Principle-Based Methodology and Real-Time Learning

What makes Senzing AI unique is its principle-based entity resolution methodology, which leverages built-in intelligence on the expected behaviors of entity data and continuously adapts through Entity Centric Learning™. As new records arrive, Senzing AI gets smarter – re-evaluating previous assertions and self-correcting in real time to align with the evolving truth in your data, even when data is inconsistent or incomplete.

Senzing AI uncovers matches and relationships that conventional methods miss. For organizations, this means fewer false positives, fewer missed matches, and faster time to insight. Whether you’re combating fraud, ensuring compliance, or improving operational intelligence, Senzing is the trusted entity resolution AI that arrives trained, gives the same answer every time, and learns in real time, delivering consistent, explainable results with accuracy that never drifts.

The Senzing Purpose-Built AI for
Entity Resolution

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.

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 Learningâ„¢, support anomaly detection and provide sequence neutral processing.

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.

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.

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.

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