Platform · Restaurant wine lists

Restaurant Wine List Data

A glass price and a bottle price are different products. Dividing one by four is not analysis.

Restaurant wine lists price the same wine twice, by the glass and by the bottle, and the pour size that links them is frequently unstated. Both prices are collected as separate fields, pour size only where the list states it, and a markup figure is computed only against a named retail benchmark with its date. Vintage is often missing or out of date on lists, and that is recorded rather than corrected. Jurisdiction, channel and the price surface sit on every record, the SKU key carries vintage or age statement, format and ABV, and no age gate is passed by misrepresenting who is asking.

A vintage printed on a list may be the last one they bought, not the one in the glass, so it is stored as printed.

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restaurant_wine_sample.jsonl LIVE FEED

{"price_glass":12.00,"price_bottle":56.00,"serve_size_ml":175} {"vintage_as_printed":2019,"price_premium_pour":"null","corkage":25.00} {"markup_vs_benchmark":3.1,"benchmark_source":"named retailer","benchmark_date":"2026-09-20"}

illustrative sample rows · Globalglass and bottle apart · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Restaurant wine lists or its owners. Restaurant wine lists and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Restaurant wine lists at a glance

How we handle Restaurant wine lists specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Prices: Glass and bottle, separate fields
Pour size: Only where stated
Markup: Against a named benchmark, dated
Vintage: As printed, even when stale
Premium pours: Third tier where offered
Corkage: Where published
Coverage: Partial, and stated
Refresh: Monthly to quarterly

Platform specifics

Glass, bottle and markup

These are the reasons a Restaurant wine lists dataset needs its own handling rather than a shared retail schema.

List conventions

Markup is a derived figure, so it carries the benchmark it came from. Without that, it is a number with no denominator.

price_glass, price_bottle and price_premium_pour kept apart.
serve_size_ml only where the list states it.
markup_vs_benchmark with benchmark_source and its date.
vintage_as_printed preserved exactly.

List databases, and what we do not do

List databases

Commercial wine-list databases and awards programmes publish venue metadata openly and list contents behind paid tiers; the paid contents are not ours to redistribute. See our on-trade page.

Price surfaces

Per-litre normalisation is carried beside the observed price rather than replacing it, and the jurisdiction that produced the price stays on the record, as the wine, spirits and liquor hub describes.

What we do not do

Redistribute a paid list database, invent a pour size, correct a printed vintage, take sommelier or customer details, or compute markup without naming the benchmark.

Scope

What we collect on Restaurant wine lists, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Glass and bottle prices apart
Premium pour as its own tier
Pour size only where stated
Vintage kept as printed
Markup with a named benchmark
Benchmark date recorded
Corkage where published
Coverage stated per market
Monthly to quarterly refresh

❌ What we do not, and why

Invented pour sizes
Corrected vintages
Markup without a benchmark
Paid list databases redistributed
Sommelier or customer details

Core Restaurant wine lists fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
venue_id / venue_name	Public listing
sku_key	Producer, wine, vintage as printed, format
price_glass / price_bottle	Kept apart
price_premium_pour	Where offered
serve_size_ml	Where stated
vintage_as_printed	Exact
markup_vs_benchmark	Derived
benchmark_source / benchmark_date	Named
corkage	Where published
currency	As listed
source / captured_at	Per record

Use cases

What teams do with Restaurant wine lists data

Markup benchmarking

On-premise against retail, by tier.

List presence

Which producers appear where.

Glass programmes

What is poured by the glass.

Vintage tracking

What lists actually print.

The 24-hour sample — run on your sources, not ours

Send us a Restaurant wine lists item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Restaurant wine lists is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Restaurant wine lists data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what wine, spirits & liquor data covers, and a Restaurant wine lists-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Restaurant wine lists data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

No. They are separate prices and stay that way.

No. It is recorded only where the list states it.

Against a named retail benchmark with its date, or not at all.

No. The printed vintage is preserved as printed.

Not for redistribution. Their paid contents are not ours to pass on.

It depends on venue counts and fields.

Talk to us.

See real Restaurant wine lists data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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