fuzzy for cannabis data quality

Your cannabis data

has ghosts in it.

Fuzzy detects ghost records, matches real products, and keeps your data clean — automatically.

Incoming POS record
nameGrene Crck 420mg flwr 1/4z
sourceDutchie POS
brandGrene
thc420mg
weight1/4z
categoryflwr
Ghost detection
Structural validation420mg THC impossible for flower
Embedding distanceNo close match in catalog
Semantic validationBrand misspelled, weight unparseable

Records flow through three checks — GHOST, SUSPICIOUS, or VALID

Millions of SKU records. Thousands of real products.Where do the ghosts come from?

Manual entry under pressure

A budtender receives a shipment and types "Grene Crck 420mg flwr 1/4z" into the POS. The brand is misspelled, 420mg THC is physically impossible for flower, and "1/4z" doesn't parse to any standard weight.

Force-matched or silently created

Most systems either force-match this garbage to the nearest catalog entry — polluting your data — or create a brand new SKU for a product that doesn't exist. Either way, analytics, menus, and inventory are now wrong.

30+ POS systems, zero standards

Cannabis has no UPCs, no universal product codes. Dutchie, Flowhub, Treez, BLAZE, Cova — each with their own data formats, each dispensary creating its own mess independently.

pos_product_feed.csv — 61,247,893 rows
product_namepos_sourcebrandthcweight
Grene Crck 420mg flwr 1/4zDutchieGrene420mg1/4z
Blue Dream 3.5gFlowhubPacific Reserve24.5%3.5g
OGK PRE ROL 1g indicaBLAZEJngle Boys31%1g
GSC 1/8 flwr hybridTreezCookies281/8
sour diesel cartridge .5Covastiiizy89.2%.5
GGGG#4 FlWR 7gm INDCDutchie???999mg7gm
Wedding Cake Budder 1gMETRCWest Coast Cure78.4%1g
impossible valuesunparseable / misspelledShowing 7 of millions of rows

Ghost records pollute analytics, menus, and inventory reports. Most systems don't catch them.

How fuzzy works

1
Ghost Detection

Detect

Catch garbage before it enters your pipeline.

Three independent checks evaluate every record before it touches your catalog.

  • Structural validation — are the attributes physically possible?
  • Embedding distance — does this record resemble any known product?
  • Semantic validation — do the attributes make sense together?

Records get classified as VALID, SUSPICIOUS, or GHOST with confidence scores and plain-language explanations.

2
Entity Resolution

Match

Connect real records to canonical products.

For records that pass ghost detection, multi-signal matching finds the right product.

  • Exact lookup, fuzzy string similarity, ML embeddings, and LLM verification
  • Confidence scores on every match
  • Configurable per organization — your categories, your naming conventions, your rules
3
Continuous Learning

Maintain

Every human decision makes the system smarter.

Confirming a ghost, correcting a match, overriding a suggestion — each action trains the model.

  • Ghost detection gets sharper over time
  • Matching accuracy improves with every correction
  • Less human intervention needed while maintaining accuracy

Your investment compounds. The system requires less work the longer you use it.

Cannabis data is uniquely broken.

No universal product codes, no industry standards, and 30+ incompatible POS systems — creating a data quality crisis that compounds with every new dispensary.

No UPCs

No universal product identifiers. Every dispensary names products however they want.

30+ POS systems

Dutchie, Flowhub, Treez, BLAZE, Cova, and dozens more — all incompatible data formats.

Manual data entry

Budtenders type product info by hand under time pressure. Typos, abbreviations, and guesses everywhere.

Regulatory fragmentation

State-by-state rules, post-acquisition platform consolidation (Dutchie acquired Greenbits + Leaflogix), and no industry-wide data standards.

Works with your POS
Dutchie
Flowhub
Treez
BLAZE
Cova
METRC

Built for your workflow

For data platforms

Headset, Dutchie, BDSA, LeafLink

  • Upstream pre-filter that catches garbage before it enters your normalization pipeline
  • Reduces wasted human review hours on records that should have been rejected at intake
  • Improves the quality of analytics products your customers pay for
  • API integration — send records, get back verdicts with confidence scores

For retailers & MSOs

Multi-location dispensary chains

  • Clean up your POS product catalog across all locations
  • Consistent naming, accurate attributes, no ghost SKUs cluttering your system
  • Better analytics, better menus, better inventory decisions
  • Works with your existing POS — Dutchie, Flowhub, Treez, BLAZE, Cova
The scale of the problem

Cannabis data is uniquely broken

K+

Dispensaries in the US

Each running their own POS, each creating their own naming mess

+

POS systems

Dutchie, Flowhub, Treez, BLAZE, Cova, and more — all incompatible

~

SKUs per dispensary

Average inventory size — most duplicating records already in the system

x

Data inflation

The gap between raw SKU records and distinct real products — that's the ghost problem

We're looking for design partners

Fuzzy is early-stage. We're working with cannabis data leaders to validate ghost detection and build the right solution.

  • Real problem, real collaboration — not a sales pitch
  • Be first to benefit from a purpose-built solution
  • Built by engineers who understand the data problem

or join the waitlist