Product Matching & Data Normalization Services.
Accurately match identical products across different e-commerce retailers, even when product titles, descriptions, and packaging differ. We combine exact barcode matching (UPC/GTIN) with machine learning fuzzy similarity models.
99.4%
Fuzzy Match Precision
ASIN $\to$ UPC
Universal Cross-Reference
Human-in-Loop
Quality Verification Gate
{
"master_entity_id": "ENTITY-BOSE-QC45",
"canonical_title": "Bose QuietComfort 45 Bluetooth Headphones",
"upc_gtin14": "00017817834520",
"matched_retailers": [
{
"retailer": "Amazon US",
"external_sku": "B098FKXT8L",
"retailer_title": "Bose QC45 Noise Cancelling Over-Ear Headset (Black)",
"price": 279.00,
"match_confidence": 0.998,
"match_method": "Barcode + Brand/MPN Match"
},
{
"retailer": "Best Buy",
"external_sku": "6471200",
"retailer_title": "Bose - QuietComfort 45 Wireless Noise Cancelling - Triple Black",
"price": 279.99,
"match_confidence": 0.995,
"match_method": "AI Fuzzy Transformer Embedding"
}
]
}Multi-Tier Matching Architecture
How We Solve the Cross-Retailer SKU Matching Problem.
Different retailers use different titles, missing barcodes, and conflicting variant structures. Our multi-stage matching engine resolves identical products with unmatched precision.
Stage 1: Hard Identifier Matching
Exact 1:1 cross-referencing on deterministic identifiers: UPC, EAN, ISBN, GTIN-12/14, Manufacturer Part Number (MPN), and Model Numbers.
Stage 2: AI Neural Text Embeddings
Transformer-based NLP models convert product titles, specifications, and brand names into dense vector embeddings to calculate cosine semantic similarity scores.
Stage 3: Computer Vision Image Match
When titles differ, visual AI models compare product packaging, angles, and color swatches to confirm that two listings represent the exact same physical product.
Stage 4: Attribute Normalization
Standardize conflicting units of measurement (e.g. converting 16 fl oz $\leftrightarrow$ 1 pt $\leftrightarrow$ 473 ml, or XL $\leftrightarrow$ Extra Large) into unified canonical schemas.
Stage 5: Human-in-the-Loop QA
Low-confidence edge cases (e.g. multi-packs vs. single items) are automatically routed to our internal data validation team for manual verification.
Stage 6: Master Product Catalog
Generate a single unified master product database that links every competitor SKU, Amazon ASIN, and Walmart URL to your canonical internal product ID.
Solve Your Cross-Retailer SKU Matching Today.
Send us a sample list of 100 of your SKUs and your top 3 competitor URLs. We'll run a free match test and show you the exact match accuracy.
Request Product Matching Pilot