This engagement focused on turning fragmented auction and retail wine data into a reliable operational platform.
The delivery combined lakehouse architecture, data pipeline design, named entity recognition for product attributes, and matching logic for noisy records.
What was built
- domain-driven data model for wine inventory and pricing flows
- medalion-style processing from raw ingestion to business-ready layers
- entity extraction for producer, vintage, label, bottle size, and condition data
- fuzzy and semantic matching to normalize records across sources
Why it mattered
The system reduced manual cleanup and created a stronger base for analytics, pricing intelligence, and operational decisions.
For NewCo360, this is a repeatable example of how data engineering and AI extraction can operate together inside a production platform.