Matcher

Unmatched record matching.

Semantic Matching

Semantic Entity and Attribute Matching

Understanding the meanings behind the strings and numbers that make up the data record is key for precise matching, to avoid false positives (similarly looking but distinct values) and false negatives (missing good matches).

Step 1 is parsing the data to understand it, splitting it up into its components. Each matcher makes use of its corresponding Parser internally.

Type-specific dictionaries and matching logic is applied, and complex result objects are returned to the API caller.

Matchers are available on every hierarchy level, like a system of Lego building blocks. For simple 1-value attributes such as a Date, to entities like a postal address. Either as plain text values in a string, or semi-structured, to fully split into designated fields.

The matcher stack is an integral part of our Search Cluster turn-key solution.

Process

How it works

1

Parse

Input is parsed into structured components. The matcher invokes its corresponding Parser internally.

2

Match

Type-specific dictionaries and matching logic compare each attribute on multiple levels.

3

Score

A complex result object with sub-scores and an overall similarity is returned to the API caller.

Bundled in Search Cluster

The matcher stack as a turn-key platform

Every matcher is also part of Search Cluster — our enterprise PII platform with encrypted storage, semantic search and continuous backup. One platform, all matchers, ready to deploy.

Explore Search Cluster
Get in touch

Match records in your stack

We're happy to support you in getting ready. We can walk you through the API, run a benchmark on your data, and help with onboarding.