Fine Dining Reservation Site with Sommelier Pairing Engine

Problem and target customer

Why this exists

Customer problem

Fine-dining teams collect reservation details in one system, dietary notes elsewhere, and wine preferences only after arrival. Guests cannot plan the full experience, while sommeliers repeat basic discovery and risk acting on incomplete or outdated notes.

Who pays

Independent fine-dining restaurants offering tasting menus, optional wine pairings, and high-touch repeat-guest service.

Business model

Monthly restaurant subscription plus an optional per-booking platform fee.

Editorial note

The pairing engine should operate inside the restaurant's current menu and available cellar list. That constraint makes its draft useful to a sommelier and prevents the polished interface from recommending a bottle the restaurant cannot serve.

Dietary notes and wine suggestions have different risk boundaries. The system can organize both, but only staff should resolve guest safety questions, and no generated text should imply that a pairing has addressed an allergy.

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What do you need next?

Evaluate the operating tradeoffs quickly, or inspect how to build the MVP.

Evaluate this ideaSee the cost and deployment preview

Compare the three operating paths at the default workload before opening the full calculator.

See cost preview Build this ideaInspect the build blueprint

Review MVP scope, workflow, states, data rules, architecture, and acceptance criteria.

Open blueprint

Evaluation preview

What would it take to run?

Directional infrastructure estimates for the current 500 bookings assumption. Open the full calculator when you are ready to change it.

Open full cost and deployment

ManagedSelected

$5.84-$36/ month

Railway hosts booking, database, webhooks, and pairing worker.

Lowest operating effort

Lean self-hosted

$7.84-$21/ month

A Hetzner cloud server runs the restaurant stack.

Lowest baseline cost

More control

$26-$91/ month

Dedicated or larger resources isolate web, database, and workers.

Most separation and control

Build blueprint

Build the first paid use case

Product goal

Who it serves and what it must change

Target user: A guest booking a tasting-menu experience and the host or sommelier preparing service.
Problem: Reservation inventory, menu choices, dietary notes, and wine preferences are not captured together early enough for accurate preparation.
Measurable outcome: A guest can reserve an eligible table, choose menu and pairing preferences, pay a deposit, and receive only a staff-approved pairing recommendation tied to that menu version.

MVP scope

What ships now and what waits

Included

Publish bookable services, table capacity, tasting-menu versions, and deposit rules.
Collect party-level dietary notes and guest wine preferences during booking.
Generate a constrained pairing draft from the current menu and wine catalog for sommelier approval.

Excluded

Restaurant-wide POS and inventory management.
Autonomous alcohol or allergy advice.
Dynamic pricing, loyalty tiers, and multi-venue optimization.

UX and user flow

Screens, actions, and states

Experience Booking

Choose date, party size, menu, seating, and dietary notes from valid availability.

Select a slotChoose menuAdd guest notes

Pairing Preview

Collect taste preferences and show the approved pairing explanation or a pending-review state.

Set preferencesRequest pairingAccept or skip pairing

Service Review

Let staff inspect booking context, edit model suggestions, and approve what the guest sees.

Review dietary notesEdit pairingApprove recommendation

Primary flow

The guest selects a valid table slot and current tasting menu.
Party notes and wine preferences are saved before hosted deposit checkout.
Payment confirmation reserves capacity and queues a structured pairing draft.
A sommelier edits or approves the draft; the guest sees the reviewed recommendation.

Loading, empty, and error states

Slot: open, held, booked, released
Reservation: pending_payment, confirmed, canceled, completed
Pairing: not_requested, generating, review_required, approved, failed
Menu: draft, active, retired

Core entity outline

Entities and business rules

ServiceSlot

Defines date, seating period, table capacity, and booking cutoff.

Fields: starts_at, capacity, held_covers, booked_covers, cutoff_at
Relations: Has reservations

Reservation

Stores party size, menu version, guest notes, deposit state, and service status.

Fields: slot_id, party_size, menu_version_id, dietary_notes, payment_status, status
Relations: Has guest preferences, May have one pairing draft

WineCatalogEntry

Provides approved labels, grapes, regions, style tags, availability, and menu compatibility constraints.

Fields: name, vintage, style_tags, available, restriction_notes
Relations: Referenced by approved pairing items

PairingDraft

Stores model input version, structured suggestions, staff edits, and approval actor.

Fields: reservation_id, model_version, suggestions_json, status, approved_by
Relations: Belongs to one reservation

Business rules

A temporary hold expires unless a verified deposit webhook confirms it.
The model may choose only active catalog entries supplied in the request.
No generated pairing reaches the guest before staff approval; dietary notes are presented separately and never treated as resolved by the model.

Architecture and data flow

Components, integrations, and controls

Booking application

Serve availability, guest checkout, preference capture, and staff review.

Capacity service

Hold and confirm covers transactionally to prevent overbooking.

Pairing worker

Send constrained menu and catalog data to the LLM and validate structured output.

Relational database

Persist menu versions, reservations, holds, profiles, and approvals.

Integrations

Stripe Checkout for deposits
OpenAI API for constrained pairing drafts
Transactional email for confirmations

Data flow

A database transaction creates an expiring hold before checkout.
A signed payment webhook confirms the reservation once and releases expired holds.
The worker sends only current menu items, available wines, and preference tags; validated output enters staff review.

Failure handling

Release an unpaid hold after timeout
Show booking confirmation without a pairing when model output fails
Keep invalid model output in an internal error state for manual staff entry

Security

Never send names, contact details, or raw free-text medical notes to the LLM
Verify payment webhooks
Restrict staff review by restaurant role
Label pairings as staff-reviewed suggestions, not health advice

Rate limits

Limit slot holds per session
Allow one active pairing generation per reservation
Throttle public availability searches

Deliverables and acceptance

Definition of done for the MVP

Deliverables

Guest booking flow
Capacity and deposit logic
Versioned menu and wine catalog
Structured pairing worker
Staff approval screen and tests

Acceptance criteria

Concurrent guests cannot confirm more covers than a slot allows.
A repeated payment webhook creates no duplicate reservation or deposit.
A draft referencing an unavailable wine is rejected before review.
If model generation fails, the booking remains confirmed and staff can enter a pairing manually.

Recommended stack

Enough technology for the first version

Application

Next.js and TypeScript

Combines public booking and protected staff review.

Database

PostgreSQL

Transactions are required for capacity holds and provider event idempotency.

Payments

Stripe Checkout

Hosted deposit collection avoids handling card data.

Pairing

OpenAI Responses API with structured output

The model drafts from a bounded menu and wine catalog while staff retain control.

Why this is sufficient

Booking correctness is the foundation; pairing is a bounded, reviewable enhancement. Keep capacity and catalog rules deterministic, and let the model draft an explanation only from supplied active records.

Not required for the MVP

Calendar APIObject storageVector databaseAutonomous agent

Next stepTurn the blueprint into an execution plan

Copy the build prompt, model the operating cost, and choose where to deploy.

Build and ship

Execution

Build, price, and deploy the MVP

Once the blueprint is clear, use the prompt, cost model, and deployment options to start building.

Build prompt

Copy this into a builder

Use a TypeScript application with PostgreSQL, Stripe Checkout, and a structured-output LLM call whose suggestions remain pending until a sommelier approves them.

Build prompt

Your build prompt is ready

Open the prompt pack whenever you are ready to take this blueprint into your builder.

Based on the blueprintReady for your builderFollow-up steps included

Cost calculator

Model the MVP operating cost

A technical run-cost estimate for the first production version. Team, acquisition, payment fees, and business COGS are excluded.

Estimated monthly total$5.84-$36

$5.84-$36 per 500 bookings

Bookings / month500 bookings

Selected pathEasiest

Pricing checkedAug 2, 2026

Cost breakdown

$5.84-$36 per month

Low and high values allow for usage variance and plan headroom.

Managed booking stack

Railway hosts booking, database, webhooks, and pairing worker.

1K bookings included, then $2-$8 per 1K bookings

$5-$35

Pairing draft generation (GPT-5.4 mini)

One constrained draft per booking from the active tasting menu and wine catalog.

500 bookings x 1.2K input + 300 output tokens

$0.84-$1.41

Reservation email

Booking, pairing-ready, and change notifications.

3K bookings included

Included

Booking and database compute
Pairing model tokens
Beta transactional email

Not included

Stripe deposit fees
Wine inventory
Sommelier labor
POS integration
Marketing and photography

Pricing basis

The estimate combines the selected infrastructure path, required operating modules, selected optional modules, and usage above included monthly allowances. Taxes and regional uplifts are excluded.

Railway pricing OpenAI model pricing Resend transactional email pricing

Deployment options

Pick the operational tradeoff

Choose based on operating preference, not only the headline price.

EasiestRecommended

Railway

Deploy booking, database, webhooks, and pairing worker together.

$5-35/month before email and model usage

Good fit

Fast setup
Managed runtime
Worker support

Limitation

Monitor resource and model usage during traffic spikes.

Open Railway

Cheapest

Vultr

Run the app, worker, and database on one small Vultr VPS with Docker Compose and explicit backups.

$7-20/month before model usage

Good fit

Low baseline
Docker portability
Predictable

Limitation

You own availability, backup, and webhook incident response.

Deploy on Vultr View the lean VPS guide

More control

DigitalOcean

Separate application, worker, data, storage, and backup responsibilities as the workload grows.

$25-90/month before model usage

Good fit

Isolation
Private network
Custom backups

Limitation

Requires active operations.

Explore DigitalOcean View the separated cloud guide

Start building

Prototype, automate, then deploy

Recommended route

01Vibe-coding builderPrototype the reservation experience

Use Lovable to test party selection, menu choices, pairing explanation, deposit handoff, and staff review screens.

Open Lovable 02AutomationRelay confirmed covers to service prep

Use Make optionally to send confirmed dietary and pairing summaries to a staff channel; staff approval stays in the product.

Open Make 03DeployDeploy booking and pairing services

Run booking, inventory rules, payment webhooks, and pairing jobs together with a relational database.

Open Railway

Fine Dining Reservation Site with Sommelier Pairing Engine

Is this worth validating?

Build this if

Avoid this if

Validate first

Why this exists

Customer problem

Who pays

Business model

What do you need next?

What would it take to run?

Build the first paid use case

Who it serves and what it must change

What ships now and what waits

Included

Excluded

Screens, actions, and states

Primary flow

Loading, empty, and error states

Entities and business rules

Business rules

Components, integrations, and controls

Integrations

Data flow

Failure handling

Security

Rate limits

Definition of done for the MVP

Deliverables

Acceptance criteria

Enough technology for the first version

Next.js and TypeScript

PostgreSQL

Stripe Checkout

OpenAI Responses API with structured output

Why this is sufficient

Not required for the MVP

Build, price, and deploy the MVP

Copy this into a builder

Your build prompt is ready

Model the MVP operating cost

Usage assumptions

$5.84-$36 per month

Included

Not included

Pricing basis

Pick the operational tradeoff

Railway

Good fit

Limitation

Vultr

Good fit

Limitation

DigitalOcean

Good fit

Limitation

Prototype, automate, then deploy

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