Extracting 500M+ Records Monthly with 99.9% Uptime SLA
1,000 Free Sample Records (24h Delivery) +1 (800) 829-DATA
AI Entity Resolution 99.4% Match Accuracy

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 and computer vision validation.

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99.4%

Match Precision SLA

ASIN → UPC

Universal Cross-Reference

Multi-Model

Text, Barcode & Vision

matched_entity_pair.json
Exact Match 
{
  "canonical_item_id": "CANON-PROD-90412",
  "upc_gtin": "885911481029",
  "master_product_title": "DeWalt 20V MAX Cordless Drill Kit",
  "match_confidence_score": 0.998,
  "resolved_cross_matches": [
    {
      "retailer": "Amazon US",
      "identifier_type": "ASIN",
      "identifier_value": "B00ET5VMTU",
      "price": 99.00,
      "method": "EXACT_UPC_CROSSWALK"
    },
    {
      "retailer": "The Home Depot",
      "identifier_type": "Internet # / SKU",
      "identifier_value": "205030281",
      "price": 99.00,
      "method": "MPN_AND_TITLE_EMBEDDING"
    }
  ]
}
Confidence: 99.8% Verified Pair
The Product Data Bottleneck

Eliminate Catalog Discrepancies Across Fragmented Retailers.

In theory, competitive price intelligence is simple: compare your product price against competitor prices. In practice, however, every e-commerce retailer describes products differently. Amazon uses proprietary ASINs, Home Depot uses Internet Numbers, Walmart uses Item IDs, and direct brands use internal SKU codes.

Furthermore, product titles are notoriously inconsistent. One merchant might list an item as "Sony WH-1000XM5 Wireless Noise Canceling Headphones - Black" while a competitor lists it as "WH1000XM5/B Sony Over-Ear Bluetooth Headset (Charcoal)". Simple string matching algorithms fail, creating false positives (matching a 1-pack with a 2-pack) or false negatives (missing exact price matches).

Our product matching and entity resolution services deploy a multi-tiered pipeline: exact universal barcode lookups (UPC/GTIN/EAN), transformer-based NLP title embeddings (BERT), attribute constraint checking (pack count, size, color), and computer vision packaging comparison to achieve 99.4% verified match precision.

Strategic Product Matching Benefits

Accurate Like-for-Like Price Comparisons

Ensure your repricing software only matches identical items, preventing disastrous pricing race-to-the-bottom errors caused by comparing single units against bundle packs.

Automated Catalog Assortment Gaps

Identify exactly which SKUs competitors are carrying that you lack, and vice versa, unlocking actionable product procurement and merchandising roadmaps.

ASIN to UPC / GTIN Crosswalk Generation

Translate Amazon ASINs into universal UPC and GTIN barcodes, allowing multi-channel retail systems to harmonize Amazon data with external ERPs.

Automated Master Data Management (MDM)

Deduplicate messy multi-vendor catalogs and merge fragmented vendor feeds into a single clean, authoritative master product record.

Enterprise Matching Pipeline

A Multi-Tiered AI Entity Resolution Architecture.

Combining deterministic barcode matching with probabilistic deep-learning similarity models.

Tier 1: Barcode Crosswalk

Deterministic matching via universal identifiers: UPC, EAN, ISBN, MPN, and GTIN-14. Resolves 60–70% of identical products with 100% mathematical certainty.

Tier 2: NLP Semantic Embeddings

Fine-tuned e-commerce BERT language models compute semantic vector similarity scores across normalized titles, brand entities, and product descriptions.

Tier 3: Attribute Constraints

Strict attribute gatekeepers enforce exact parity: pack quantity (1-pack vs 3-pack), volume (12oz vs 16oz), colorway, and model year to prevent erroneous pairs.

Tier 4: Computer Vision Verification

Convolutional neural networks (ResNet / Vision Transformers) compare product gallery photography, packaging artwork, and visual logos to corroborate match confidence.

Tier 5: Human-in-the-Loop QA

Borderline statistical matches (confidence scores between 85% and 94%) are routed to human data specialists for rapid manual verification and model retraining.

Tier 6: Master SKU Harmonization

Deliver a unified relational database crosswalk linking your internal master catalog SKUs to external retailer URLs, ASINs, and live price endpoints.

Structured Data Dictionary

Product Matching Data Attributes & Schema.

All matched product pairs are validated and delivered with confidence scores:

Field Name Data Type Description Example Value
source_sku String Your internal catalog product identifier. "SKU-DEW-DCD777"
matched_retailer String Domain of the matched competing retailer. "amazon.com"
matched_product_id String Target retailer item identifier (ASIN, Item ID, or SKU). "B00ET5VMTU"
match_confidence_score Float Statistical probability score ranging from 0.000 to 1.000. 0.998
resolution_method String Method: "EXACT_UPC", "MPN_MATCH", "NLP_EMBEDDING", or "IMAGE_SIMILARITY". "EXACT_UPC"
upc_match Boolean True if match was validated by identical universal barcode. true
attribute_parity_verified Boolean True if pack count, size, and color constraints passed validation. true
matched_url String Direct canonical URL of the matched competitor product page. "https://amazon.com/dp/B00ET5VMTU"
Got Questions?

Frequently Asked Questions: Product Matching.

Answers regarding fuzzy matching, barcode resolution, and catalog crosswalks.

When barcodes are omitted, our multi-modal machine learning pipeline evaluates NLP title embeddings (BERT), normalized brand entities, manufacturer part numbers (MPN), and computer vision packaging comparison, applying strict attribute filters (pack size, weight, color) to achieve 99.4% precision.
We implement strict attribute constraint gatekeepers. Even if titles share 98% string similarity, our algorithm extracts and validates numerical pack sizes, volume metrics (oz, ml), and variant colorways. If pack count or volume fails to match, the match candidate is automatically rejected.
Yes. We maintain extensive crosswalk databases that link millions of Amazon ASINs to their corresponding manufacturer UPC, EAN, and GTIN barcodes, enabling seamless cross-channel price comparison and inventory tracking.
For borderline matches where AI model confidence falls between 85% and 94%, our system routes candidate pairs to expert human data reviewers who verify or reject the match, feeding the feedback back into the model to improve future precision.
Our distributed cloud architecture processes millions of SKU candidate comparisons per hour using GPU-accelerated vector databases (Pinecone, Milvus) and automated blocking algorithms that eliminate pairwise quadratic comparison overhead.
We deliver crosswalk mapping tables in CSV, Parquet, or JSON format directly into your cloud data warehouse (Snowflake, BigQuery, PostgreSQL) or via real-time REST API lookup endpoints.

Start Harmonizing Your Product Catalog Today.

Submit a sample list of 500 SKUs from your catalog. Our AI entity resolution pipeline will match them against target competitor websites with verified confidence scores in 24 hours.

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