fuzzy-matching

Fuzzy matching reconciles similar inventory items by using a three-stage pipeline: PostgreSQL trigram pre-filtering, salient overlap checks, and multi-factor similarity scoring to identify and match vendor SKUs and product records.

Yes, the fuzzy matching pipeline requires a PostgreSQL database with the pg_trgm extension installed and a normalized_name column to execute trigram pre-filtering and multi-factor similarity scoring successfully.

The best way to match vendor SKUs is using a Python and SQL-based normalization layer paired with scalable scoring routines to process product names and reconcile records from multiple data sources.

Multi-factor similarity scoring works by applying a Python-based normalization and scoring pipeline that evaluates salient overlap after an initial PostgreSQL trigram pre-filter identifies potential inventory matches.

Yes, you can use this fuzzy matching pipeline to deduplicate product entries by running the FuzzyItemMatcher against a target item name to retrieve the top matching inventory items for reconciliation.

Trigram pre-filtering for data reconciliation requires a normalized_name column and the pg_trgm extension, meaning it is limited to PostgreSQL environments configured for text similarity indexing and scalable scoring routines.