Posidex Customer Entity Resolution and Matching vs Splink

Posidex Customer Entity Resolution and Matching links customer records across systems and data sources to create a unified customer view. Delivered through the AI/ML-powered PrimeMDM platform, it classifies records as the same, possibly the same, related, or possibly related. For onboarding, it can compare demographic data with KYC details in real time, validate submitted information, and cross-reference identity documents and utility proofs including PAN, Aadhaar/SSN, passports, voter IDs, driving licenses, and utility bills. Its engine assigns authenticity scores, and teams can configure granular match rules across multiple parameters. Organizations can upload bulk batches without constraints on the number of fields. Posidex says the solution can manage 5 billion records in one go with 99.5% match accuracy, and can be implemented in as little as three months. API integration is described as providing fast results with minimal latency. The product can find records across enterprise systems, apps, and knowledge bases on premises or in the cloud. Posidex describes global search as federated and encrypted, and the offering as designed for privacy. Pricing is on request; the site offers demos and sales contact. Supported deployment platforms include API, Linux, macOS, self-hosted, web, and Windows.

Splink is a free Python package for probabilistic record linkage: it deduplicates and connects records without unique identifiers. Its core algorithm is based on the Fellegi–Sunter model, and it can train models without training data using an unsupervised approach. Matching supports term-frequency adjustments and user-defined fuzzy logic. Interactive outputs help users inspect models and diagnose linkage problems. Splink documents DuckDB, Spark, PostgreSQL, and SQLite backends; DuckDB is installed by default, while Spark and PostgreSQL have optional installs. The project says it can run with big-data backends such as Spark on datasets of 100 million or more records, and can link a million records on a laptop in around a minute. It works best with standardised data containing multiple columns that are not highly correlated. Installation is through pip or conda and requires Python 3.10 or later. The project lists Linux and self-hosted use and an MIT license.