One wine record · six outputs
Sample dataWine Data
Canonical record
Vinfra AI
Wine-trained
Product Description
Commerce
SEO Metadata
Search
Tasting Note
Editorial
Translation
6 locales
Social Content
Campaigns
Image
Bottle shot
Fields grounded
18/18
Human review
1 flagged
Locales
6
The problem
Wine data arrives as messy text: supplier lists, PDFs, label photos, email offers. Turning "Ch. Lynch Bages 2016 6x75 IB" into a structured, sellable product with correct appellation, classification and duty status is slow manual work. Generic language models make it fast and wrong — inventing appellations, mismatching vintages and hallucinating scores.
The solution
Our intelligence layer is constrained by a wine knowledge model. Extraction, enrichment and generation are grounded in canonical producer, estate, cuvée, appellation and classification records, and anything the model cannot resolve is flagged for a human rather than guessed. Content generation inherits that same grounding.
One wine record · six outputs
Sample dataWine Data
Canonical record
Vinfra AI
Wine-trained
Product Description
Commerce
SEO Metadata
Search
Tasting Note
Editorial
Translation
6 locales
Social Content
Campaigns
Image
Bottle shot
Fields grounded
18/18
Human review
1 flagged
Locales
6
Inside the product
A wine-specific intelligence layer.
- Data enrichment & recognition
- Multilingual content generation
- Visual AI for product imagery
- Commerce & marketing automation
Capabilities
What it does.
01
Data enrichment & recognition
Parse offers, supplier files and label images into structured wine records with producer, cuvée, vintage, format, appellation and classification resolved. Ambiguous matches are surfaced with candidates instead of silently resolved.
02
Multilingual content generation
Produce tasting notes, product descriptions and campaign copy in English, German and French from verified attributes. Terminology follows wine convention in each language rather than literal translation.
03
Visual AI for product imagery
Generate consistent catalogue imagery and clean up label photography at scale, keeping vintage and format accurate on every asset. Output follows your art direction, not a generic template.
04
Commerce & marketing automation
Draft offers, allocation emails and merchandising blocks from live stock and pricing data. Everything is reviewable before it ships.
05
Grounded, not guessed
Generation is bound to your data and the wine knowledge model, with source attribution on factual claims. Where evidence is missing, the system says so.
06
Human review workflow
Confidence scores, side-by-side diffs and approval queues keep a person on the last step. Corrections feed back into your catalogue.
How it works
From your data to running operations.
- Step 1
Point it at your data
Connect catalogues, supplier feeds, offer emails and label images.
- Step 2
Resolve and enrich
Records are matched to canonical wine identities and completed with missing attributes.
- Step 3
Generate
Produce notes, descriptions, translations and imagery from verified fields.
- Step 4
Review and publish
Approve in a queue, then push to commerce, CRM and marketing channels.
Why wine-specific
Not adapted. Built for this.
A general model has read about wine. It has not been constrained by wine. Ask it for a 2015 tasting note and it will happily describe the 2016, invent a classification or attribute a score to a critic who never tasted it. In this category, a confident error is a returned order or a compliance problem. Our layer resolves against real wine records and refuses to fill gaps it cannot support.
Integrations
Fits the stack you already run.
AI is API-first and connects through Connect to the system categories below. Named platform integrations are confirmed during scoping.
- ERP
- CRM
- Webshop platforms
- PIM / DAM
- Marketing automation
- Translation workflows
Use cases
How it shows up in practice.
Merchants
Turn a supplier offer list into publishable products in minutes instead of days.
Marketplaces
Normalise thousands of seller listings into one clean, searchable catalogue.
Hospitality
Keep multilingual wine lists accurate across venues and vintage changes.
Proof
Running in a live wine business.
AI runs inside Vinesia's own fine wine operation. Verified performance figures are published here once confirmed.
See AI against your own data.
A 30-minute session with someone who knows both the software and the wine trade.