Entity Resolution at Scale: Deduplicating Millions of Unstructured E-Commerce SKUs
Matching unstructured product listings across multiple retail platforms using character-level embeddings and vector-assisted record linkage.
One of the hardest problems in retail data science is cross-marketplace entity resolution. The exact same SKU will appear with different titles, variant spellings, omitted pack sizes, and distinct image angles across platforms:
99.1%
Matching Precision via Fine-Tuned DeBERTa-v3 Models
15M+
Cross-Platform SKU Linkages Resolved Daily
45ms
Per-Pair Inference Latency on Quantized TensorRT Runtimes
The Multi-Stage Matching Architecture
Step-by-Step Resolution Strategy
- Deterministic Attribute Blocking: Grouping candidate matches using brand tokens, numeric weight extraction, and fuzzy volume regexes to narrow comparisons from billions to hundreds.
- Dense Vector Generation: Converting normalized product titles into semantic vectors using fine-tuned mini-transformers.
- Cross-Encoder Classification: Passing candidate pairs to a neural cross-encoder outputting a unified match confidence score between 0.00 and 1.00.
Turn Raw Market Data into Actionable Catalogs
Enhance Tech Solutions builds enterprise data science engines that transform messy, unstructured web extractions into unified master datasets.