Same producer, same words, different wine
This is why exact matching and fuzzy matching both fail on wine. Only two of these three entries are the same bottle.
"Prestige Initiale" and "Prestige Initiale Grand Cru" are the same wine: same producer, region and vintage, just a classification word and a "Domaine" prefix apart. The Champagne bottle shares the producer name and "Grand Cru" but is a different wine entirely. Telling these apart means understanding wine, not just comparing characters.
Why wine catalogues break your ERP
The mess is specific to wine and beverage, and it is why generic import tools give up.
The same wine, several times
One bottle arrives as three entries across import files. Stock reads zero while the wine is in the cellar, and you lose the sale.
Producer name variants
"François Girard", "Domaine François Girard", "F. Girard". Standard French practice, and it breaks every exact match.
Vintages and cuvées that almost match
A missing vintage, a "Grand Cru" present on one line and not the other. Close enough to confuse a tool, different enough to matter.
Multiple units of measure
Bottle, case, magnum, by the glass. The same product counted and priced in different units across suppliers.
Weekends lost to reconciliation
Every new distributor file means evenings and weekends matching names by hand before anything can be imported.
Fuzzy matching you cannot trust
Similarity scores flag genuinely different wines as duplicates. You cannot auto-merge, so a human ends up checking everything.
AI that understands wine, not just strings
Context-aware matching on producer, region, vintage and cuvée, with a confidence score on every decision.
Context-aware deduplication
We match on producer, region and vintage together, so "Prestige Initiale" and "Prestige Initiale Grand Cru" are recognized as one wine, not two.
Producer normalization
"Domaine François Girard" and "François Girard" collapse to one producer automatically. No manual mapping table to maintain.
A confidence score on every match
Each proposed merge comes with a score. A near-duplicate at high confidence is surfaced with the suggestion ready to approve.
One-click review
Your team validates or rejects each proposed merge in an Excel-style sheet. Approve, override, done. AI proposes, humans approve.
Exports in your ERP's format
Clean, standardized records out as CSV, Excel or JSON, in the shape your wine ERP expects. No lock-in.
Nothing imports unreviewed
Every record is checked by someone before it reaches your system. The pipeline does the heavy lifting, a human owns the commit.
This is the wine and beverage side of the same data-cleanup pipeline we run for any ERP.
From chaotic spreadsheet to import-ready
Four steps, however many thousand entries you have.
Send us the catalogue
Any format your suppliers gave you: spreadsheets, exports, multiple files. Tens of thousands of rows is normal.
The AI clusters and matches
It groups likely duplicates, normalizes producers and cuvées, and scores each match on producer, region and vintage.
You review and approve
Flagged merges land in a review sheet with the suggestion pre-filled. Your team approves or overrides in seconds.
Import clean data
Download the deduplicated, standardized catalogue, ready to import into your ERP with no reconciliation left to do.
We have done this at scale
The real project behind this page: a Bordeaux wine ERP with tens of thousands of messy entries.
How we helped a wine ERP stop losing weekends
The full case study: 30,000+ entries, the pipeline we built, and the Excel review workflow that put a human on every merge.
What 30,000 messy wine entries actually look like
Real examples from the catalogue: near-duplicates, producer variants, and why a similarity score is not enough.
Frequently asked questions
How do you tell near-duplicate wines apart when the names differ?
We match on producer, region and vintage together, not on the name string alone. If those align but the cuvée name differs by a classification word like "Grand Cru", it is flagged as a likely duplicate with a confidence score for your team to confirm.
What about producer variants like "François Girard" and "Domaine François Girard"?
Handled automatically. The pipeline recognizes that the "Domaine" prefix is standard French practice and treats both as the same producer, so they do not create separate catalogue entries.
What if a wine has no vintage listed?
Vintage is one signal among several. If everything else matches but the vintage is missing on one line, we flag it for review rather than guess, and your team decides whether it is the same wine.
Can you handle tens of thousands of entries at once?
Yes. The approach was built on a real Bordeaux wine ERP with more than 30,000 messy entries. The pipeline processes the whole catalogue in one pass and scores every match.
How is this different from fuzzy matching?
Fuzzy matching scores string similarity, so it flags different wines that share words and misses duplicates written differently. We match on wine attributes and only propose a merge when producer, region and vintage support it.
Do we stay in control of the merges?
Always. The AI proposes; a person on your team approves every merge in a review sheet before anything is imported. Nothing reaches your ERP unreviewed.
Is this an ERP for wine businesses?
No — we are not an ERP for wine. We clean, deduplicate, and standardize wine catalogue data so it imports cleanly into the wine or beverage ERP you already run. If you are choosing an ERP for a wine business, our cleanup fits in front of whichever system you pick.
Can you help migrate our wine catalogue into a new ERP?
Yes. Whether you are switching wine ERPs or setting one up for the first time, we clean and standardize the catalogue from your old system, spreadsheets and supplier files, then export it in the format your new ERP expects. The migration lands as deduplicated, import-ready data with a human sign-off on every merge.
Cleaning up a wine catalogue?
Send us a sample of your messiest export. We will show you what a clean, import-ready version looks like.
