An AI pipeline reconciling hundreds of partner restaurants across four sources and 10+ languages

The challenge

A leading global beverage company maintained a directory of partner restaurants across Europe, but the data came from four different sources, each in different formats and over 10 languages. Reconciliation was manual, slow, and full of duplicates.

The solution

We built an AI pipeline that ingests restaurant data from all four sources, normalizes it, deduplicates using fuzzy matching across languages, and enriches entries with missing metadata. A review dashboard lets the team verify AI decisions before publishing.

The impact

The pipeline reconciled hundreds of partner restaurants across four sources and 10+ languages, reducing manual reconciliation effort by over 80% and improving data quality significantly.

Technologies used