fuzzy

Same product.

Dozens of names.

One canonical record.

Intelligent data matching that gets smarter as you use it.

Live matching
Incoming records
Blonde Roast 12oz Whole Bean
BLONDE ROAST - 340g - WHOLE BEAN
blonde-roast-12oz-wb
Blonde Roast Whole Bean (12 oz)
COFFEE_BLONDE_340G_WB
Blonde Rst WB 12oz
blonde_roast_whole_bean_340
Canonical output
Unified Record
IDf47ac10b-58cc-4372-a567-0e02b2c3d479
nameBlonde Roast
size340g
formatWhole Bean
categoryCoffee

The same record, described 50 different waysSound familiar?

Manual matching is draining your team

Data teams spend countless hours manually matching and categorizing records that should be automated.

Offshore mapping is expensive and error-prone

Entire teams hired just to map SKUs to UPCs — slow, costly, and never quite right.

Every new source compounds the mess

Each new retailer, brand, or data feed makes the problem exponentially worse.

Inconsistent data undermines everything

Ads, analytics, and operations all suffer when your underlying data can't be trusted.

Competitors with clean data move faster

While you're still mapping, they're shipping features and winning deals.

How Fuzzy works

Multi-signal matching

Combines fuzzy string matching, ML, and AI to catch what rule-based systems miss.

Confidence scores on every match.

Intelligent onboarding

Our AI agent asks relevant questions about your data via Slack or email — only during initial learning and when new edge cases appear.

Your answers apply everywhere, instantly.

Human-in-the-loop learning

Low-confidence matches surface for review. When your team confirms or corrects, Fuzzy learns — that match never needs to be made again.

The system gets smarter as you use it.

Works with your data stack
❄️Snowflake
🐘PostgreSQL
🐬MySQL
📊BigQuery
⚡Databricks
🔴Redshift

Intelligent onboarding

Questions arrive where your team already communicates — during initial learning and when genuinely new patterns emerge.

# data-ops
FuzzyAPPjust now

I'm seeing weight described as "500ml", "0.5L", "half-liter", and "16.9 fl oz" across your dataset. Should I treat these as equivalent?

Prove it's working.

Your data team has been burned before. Fuzzy doesn't ask for trust — it shows receipts.

Match accuracy metrics

See precision and recall on your actual data, not synthetic benchmarks.

Confidence distribution

Understand exactly where the system is certain and where it needs help.

Edge cases surfaced

Automatically identifies the tricky matches that need human review.

Before/after comparisons

See your data quality improve in real-time as Fuzzy learns.

Regression monitoring

Ongoing alerts if match quality ever dips — catch issues before they compound.

Who it's for

If you're aggregating data from multiple sources with no enforced standards, this is for you.

Retail & POS platforms

Unify product catalogs across hundreds of retailers with different naming conventions.

Marketplace aggregators

Match listings across platforms where the same product appears under different titles.

Healthcare data platforms

Standardize patient, provider, and facility records from disparate systems.

Supply chain & logistics

Reconcile SKUs, part numbers, and vendor codes across your entire network.

Financial services

Deduplicate customer records and match transactions across institutions.

We're looking for design partners

Fuzzy is early-stage. We're seeking companies with painful data unification problems who want to shape the solution with us.

  • Real problem, real collaboration
  • Be first to benefit from a purpose-built solution
  • No pitch deck — just a conversation
Become a design partner