Same product. Every retailer names it differently. One canonical record.
Organic Valley Whole Milk appears as "ORG VALLEY WHL MLK GALLON" in one retailer’s feed, "Organic Valley Milk, Whole, 128 fl oz" in another, and "OV Whole Milk 1 Gal" in a distributor system. Across hundreds of retailers and thousands of SKUs, Fuzzy unifies them automatically.
Organic Valley Whole Milk 1 GalORG VALLEY WHL MLK GALLONOrganic Valley Milk, Whole, 128 fl oz
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Organic Valley Whole MilkDairy1 galOrganic Valley
The Problem
Every retailer formats differently. Kroger, Walmart, Whole Foods, Instacart — same product, four naming conventions.
Abbreviations are everywhere. “ORG”, “WHL”, “MLK”, “XVOO” — truncated names that only insiders understand.
Pack sizes and volumes diverge. “1 Gal”, “128 fl oz”, “3.78L” — same quantity, three formats.
Dirty catalog data breaks promotions. Market share analysis, trade promotions, and shelf analytics all require unified product data.
Self-improving. Every correction trains the system. Adding the 20th retailer feed is faster than the 3rd.
Fully explainable. See exactly why every match was made — confidence scores, tier breakdown, reasoning. No black boxes.
Category and size veto fields. Auto-reject when category or volume mismatches — “Whole Milk 1 Gal” never matches “Skim Milk 1 Gal”.
Deeply configurable. Tune product matching rules, volume normalization, veto fields, and confidence thresholds — every retailer’s catalog is different.
Four-Tier Matching Pipeline
T1
Exact Match — Normalized
Free
T2
Fuzzy Consensus — 3/5 vote
Free
T3
LLM Reasoning — Ambiguous pairs
~$0.001
T4
Human Review — Feedback loop
You
80% free15% LLM5% human
LLM + Human Review
The LLM understands that “1 Gal” = “128 fl oz” = “3.78L” and that “TJ’s” = “Trader Joe’s”. It resolves heavily abbreviated retailer codes against full product names by reasoning about brand, category, and pack size. Human reviewers handle the final 5%, and every decision is permanently cached.
“TJ’s XVOO 500ml” → “Trader Joe’s Extra Virgin Olive Oil 500ml” — LLM expands abbreviations with 0.91 confidence
Intelligent Industry Warm-Up
Fuzzy learns your domain by asking targeted questions — not generic setup wizards. One answer applies across the entire dataset.
“Seeing ‘TJ’s’ and ‘Trader Joe’s’ across 500 records — confirm these map to the same brand?”
Your team's expertise stays with you. Fuzzy just learns from it.
Design Partner Pilot
Run Fuzzy against your actual retailer/distributor feeds — real results, not synthetic benchmarks
Field mapping tuned to your catalog schemas and retailer formats
Shape the product roadmap alongside your data team’s needs
Early access pricing — locked in before general availability
Let’s see if Fuzzy fits your grocery data.
30-minute call — we’ll look at your catalog unification pain together.