Same product. Fifty suppliers. One canonical record.
The same item appears as "iPhone 15 Pro 256GB Black" from one supplier, "IPHONE-15PRO-256-BLK" from another, and "Apple iPhone 15 Pro (256, Black)" in your warehouse system. Across thousands of products and dozens of vendors, manual reconciliation is unsustainable. Fuzzy automates the entire process.
iPhone 15 Pro 256GB BlackIPHONE-15PRO-256-BLKApple iPhone 15 Pro (256, Black)
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iPhone 15 ProElectronics256GBApple
The Problem
Every supplier has their own SKU format. Internal codes, vendor codes, UPCs — same product, zero overlap.
Manual reconciliation can’t scale. Onboarding a new supplier means weeks of mapping before a single order.
Dimension and spec mismatches compound errors. Same product listed with different units, rounding, or missing fields.
Inventory visibility depends on clean data. Duplicate products inflate stock counts and break demand forecasting.
Why Fuzzy
SKU-format-aware. Handles hyphens, underscores, concatenated codes, and stripped whitespace across vendor formats.
Self-improving. Every correction trains the system. Onboarding the 10th supplier is faster than the 2nd.
Fully explainable. See exactly why every match was made — confidence scores, tier breakdown, reasoning. No black boxes.
Dimension and weight validation. Veto fields auto-reject when specs don’t match, preventing costly inventory errors.
Deeply configurable. Tune SKU matching rules, dimension handling, veto fields, and confidence thresholds — every supply chain 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 “WH-1000XM5” and “Sony Noise Cancelling Headphones XM5” are the same product — reasoning beyond string similarity. It resolves packed-format SKUs against human-readable names. Human reviewers handle edge cases, and every decision is permanently cached.
“WH-1000XM5” → “Sony WH-1000XM5 Headphones” — LLM resolves model code with 0.93 confidence
Intelligent Industry Warm-Up
Fuzzy learns your domain by asking targeted questions — not generic setup wizards. One answer applies across the entire dataset.
“Are size suffixes (S/M/L) on ‘Nike Air Max 90’ distinct products or color/size variants of the same item?”
Your team's expertise stays with you. Fuzzy just learns from it.
Design Partner Pilot
Run Fuzzy against your actual supplier catalogs — real reconciliation, not synthetic demos
Field mapping tuned to your vendor SKU formats and warehouse schemas
Shape the product roadmap alongside your ops team’s needs
Early access pricing — locked in before general availability
Let’s see if Fuzzy fits your supply chain data.
30-minute call — we’ll look at your SKU reconciliation pain together.