Meni Use Cases

Real-world scenarios for implementing a digital menu and automation in the restaurant business.

Case 1. A café switches from a paper menu to QR#

Situation#

A small café with 40 seats. The paper menu is printed once a month; any price change or adding seasonal items means extra costs and waiting for the print shop.

Solution with Meni#

1. Uploaded photos of the paper menu → AI recognized all items automatically
2. Edited descriptions, added dish photos (some — AI-generated)
3. Placed QR codes on tables (stickers, table tents)
4. Set up 2 languages: Georgian + English for tourists

Result#

• Menu updates — in 30 seconds instead of 3–5 days
• Printing savings: ~200₾/month
• Tourists read the menu in their own language → average check up by 15%

Case 2. A restaurant launches online orders for delivery#

Situation#

A Georgian cuisine restaurant wants to accept delivery orders but isn't ready to pay a 25–30% aggregator commission (Glovo, Wolt).

Solution with Meni#

1. Created a digital menu with photos and descriptions
2. Enabled "Delivery" mode — the guest enters an address
3. Set up 3 delivery zones: free (up to 3 km), 5₾ (3–7 km), 10₾ (7–12 km)
4. Connected Stripe for online payments
5. Shared the link via Instagram, Google Maps, and business cards

Result#

• 0% commission (instead of 25–30% to aggregators)
• Own customer base for repeat orders
• Average time from order to confirmation: 45 seconds
• After 3 months — 35% of orders come through the owned channel

Case 3. A restaurant chain manages 5 locations#

Situation#

A chain of 5 restaurants: 3 in Tbilisi, 1 in Batumi, 1 in Kutaisi. Different menus, different prices, but one brand.

Solution with Meni#

1. Created the chain owner's master account
2. For each location — a separate menu with local prices
3. Shared items are inherited from a template; unique ones are added locally
4. Roles: owner → administrators (1 per city) → shift managers → staff
5. A unified analytics dashboard across the entire chain

Result#

• Launching a new item across all 5 locations in 2 minutes
• Comparing revenue and dish popularity between locations
• ABC analysis helped remove 12 low-margin items → profit up by 8%

Case 4. A hotel implements room service via QR#

Situation#

A boutique hotel with 30 rooms. Room service is taken by phone — guests complain about language barriers, order mistakes, and long wait times.

Solution with Meni#

1. A QR code in every room (on the bedside table)
2. Guest scans → sees the menu in their language (up to 45 languages)
3. Selects dishes, enters room number → order instantly goes to the kitchen
4. Set up a night menu (23:00–07:00) with a limited assortment
5. The cost is charged to the room bill

Result#

• Order errors: from 15% to 1%
• Average time from order to delivery: down by 40%
• Number of room-service orders: up by 60% (guests aren't shy about ordering via phone)
• Additional revenue: +2,500₾/month for 30 rooms

Case 5. A bar speeds up service during peak hours#

Situation#

A popular bar. On Friday–Saturday, the line at the bar counter is 10–15 minutes. Guests leave without waiting.

Solution with Meni#

1. QR codes on every table and at the bar counter
2. Guest scans → selects drinks → pays online
3. Bartender sees the order on a screen (KDS) → prepares → guest gets a push: "Your order is ready"
4. For repeat orders: a "Repeat" button in order history

Result#

• Lines reduced by 70%
• Table turnover: +2 orders/evening per table
• Average check up by 22% (easier to order another cocktail via phone)
• Bartenders focus on preparation, not taking orders

Case 6. A pizzeria with a multilingual menu for tourists#

Situation#

A pizzeria in central Tbilisi. 70% of guests are tourists from different countries. The paper menu is only in Georgian and English; waiters don't speak Arabic, Hindi, Chinese.

Solution with Meni#

1. Created the menu in Georgian → AI automatically translated it into 27 languages
2. Added descriptions: ingredients, weight, allergens, calories
3. AI photos for each item (pizza, pasta, salads)
4. The system detects the guest's browser language and shows the menu in that language

Result#

• Guests from 50+ countries read the menu without help from a waiter
• Order time reduced: from 8 to 3 minutes
• Misunderstanding-related errors down by 90%
• More Google Maps reviews (guests mention convenience)

Case 7. A restaurant implements a loyalty program#

Situation#

A restaurant wants to increase guest retention (currently only 20% return).

Solution with Meni#

1. Enabled points: 5% of the paid check comes back as points, redemption capped at 50% of the check
2. Excluded the "Alcohol" category from earning and set a minimum check amount
3. Added a stamp card for coffee: the sixth drink is free
4. Enabled the regular-guest discount — after 10 completed orders the till suggests 10% to the cashier
5. Birthday promo codes: 20% discount (automatic campaign)

There are no tiers ("bronze → silver → gold") and no "bring a friend" referral program in the loyalty engine: earning is the same for every participant. Bringing in guests for a commission is a different tool — the affiliate program for site owners and bloggers.

Result#

• Guest retention: from 20% to 45% in 4 months
• Average check of returning guests: +30% vs new guests
• The 50% redemption cap kept the program in check: points do not eat into revenue
• Guest LTV (Lifetime Value) increased 2.5x

Case 8. A café with a floor plan and reservations#

Situation#

An 80-seat café with a terrace. Guests call to book — the administrator writes it down in a notebook; double bookings and confusion happen.

Solution with Meni#

1. Created a floor plan: main hall (15 tables), terrace (10 tables), VIP (3 tables)
2. Enabled online reservations via the website and QR
3. Auto-confirmation for regular tables, manual confirmation for VIP
4. Email reminder to the guest 2 hours before the visit (the lead time is configurable, 1 to 168 hours)
5. Reservation deposit: a flat amount or an amount per guest, free cancellation until a set hour; if the guest does not show up the deposit is forfeited, and a manager can waive it with one button

There is no guest no-show counter that would close online booking after N misses — discipline is held by the deposit and the "No-show" status in the reservations log. Reminders also declare an SMS channel, but SMS are currently not delivered — a fix is in progress.

Result#

• Double bookings: from 5–7 per week to 0
• No-shows: down from 25% to 8% (thanks to reminders)
• Weekday terrace occupancy: +40% (guests see availability online)
• Administrator saves 2 hours/day managing reservations

Case 9. A food court with multiple food outlets#

Situation#

A food court in a mall: 8 food outlets (burgers, sushi, pizza, Georgian cuisine, desserts, etc.). Each outlet operates independently; there is no unified ordering system.

Solution with Meni#

1. One QR code on each table → the guest sees all 8 outlets in one app
2. One cart for all outlets: the guest picks items from different kitchens and submits them in one tap
3. The cart splits into a separate order for each outlet, all linked by a shared group number (GRP-…); an outlet sees only its own items on its KDS screen
4. The host's commission is stamped into every order and stays invisible to the guest; the settlements between the venues themselves happen outside the platform — in cash or by invoice
5. The guest gets a notification when each order is ready

Result#

• Guests order from 2–3 outlets at once (previously they went to only one)
• Food court average check: +45%
• Lines at cash registers disappeared (everything via QR)
• Mall management sees real-time analytics for the entire food court

Case 10. A pastry shop launches cake pre-orders#

Situation#

A pastry shop takes cake orders via Instagram and phone. It's hard to track: who ordered, what, for when, and whether there was a prepayment.

Solution with Meni#

1. Created a cake catalog with photos, descriptions, and price per kg
2. Pre-order form: date, size, inscription, decor, allergens
3. Full online payment for the pre-order via Stripe (an order has no partial prepayment — a percentage is taken only for a table reservation or a service appointment)
4. Automatic notification to the pastry chef about a new order
5. Guest receives status updates: accepted → in progress → ready → picked up

Result#

• Lost orders: from 10–15% to 0%
• Average time to take an order: from 15 minutes (chatting) to 2 minutes
• Payment upfront → zero cancellation rate
• The pastry chef sees the order schedule a week ahead

Case 11. A university cafeteria speeds up lunch#

Situation#

A university cafeteria: 500+ students during one lunch hour. Huge lines; students don't have time to eat between classes.

Solution with Meni#

1. Students open the menu via QR/link → pre-order (on the way to lunch)
2. Pre-order 15–30 minutes ahead → kitchen prepares for arrival
3. Every slot has its own capacity: a filled slot shows up as "Full", so the guest picks a neighbouring time and the load spreads itself across the hour
4. Meals on the university's corporate account: the student has a personal ledger with a code and a daily limit, and the spending lands on the organization's account

Result#

• Student lunch time: from 35 minutes to 10 minutes
• Kitchen throughput: +60% (pre-orders distribute the load)
• Food waste: -25% (kitchen knows volumes in advance)
• Student satisfaction: from 3.2 to 4.7 out of 5

Case 12. A restaurant optimizes food cost through analytics#

Situation#

A restaurant doesn't understand why profit is low despite good revenue. There's no control over cost of goods, ingredient write-offs.

Solution with Meni#

1. Filled in recipe cards for all 80 menu items
2. Set up automatic ingredient write-off upon sale
3. Enabled ABC analysis: A (hits) / B (average) / C (outsiders)
4. Food cost monitoring — a dedicated metric in the "Finance" section (target: 25–30%)
5. Every week they reviewed class C: recipe-card cost against the menu price

There is no threshold alert like "food cost above 35% on this item": the metric is read in "Finance", while automatic signals come from Inventory — on a low stock level.

Result#

• Food cost: from 38% to 27% in 2 months
• Identified 8 items with margin < 15% → recipes revised
• Spoilage write-offs: -40% (thanks to inventory control)
• Net profit: +11% with the same revenue

Case 13. A takeaway coffee shop without a cashier#

Situation#

A small coffee shop (10 m²). One barista does everything — makes drinks, takes orders, handles payments. During rush hour — chaos.

Solution with Meni#

1. QR code at the counter and at the entrance → guest orders themselves
2. Online payment → no cash handling
3. Barista sees the order queue on a tablet
4. Queue screen at the counter: the order number moves from the "Preparing" column to "Ready" (order contents are never shown on a public screen — only the number)
5. Repeat order: guest opens history → "Repeat my usual"

Result#

• Barista makes 40% more drinks (no distractions at the register)
• Order errors: almost 0 (guest selects themselves)
• Average check: +18% (people add dessert to coffee when they see photos)
• The line moves 2x faster

Case 14. A restaurant uses a stop list and menu scheduling#

Situation#

A restaurant with breakfasts, business lunches, and dinners. Waiters forget to warn about sold-out items — guests order and then get disappointed.

Solution with Meni#

1. Set up a menu schedule: breakfast (08:00–11:00), lunch (11:00–16:00), dinner (16:00–23:00)
2. Stop list: manager removes an item with one click → it is instantly hidden for all guests
3. Auto-stop when inventory reaches zero
4. A daily "what to order" digest for the owner — push and email for every product that fell below its reorder point

Result#

• "Sorry, it's sold out" refusals: from 8–10 per day to 0
• Scheduled menu switching: fully automatic
• Revenue loss due to stopped items: -60% (early notification → timely purchasing)
• Guest satisfaction: significant increase (no disappointments)

Case 15. A franchise uses a whitelabel solution#

Situation#

A chain of 20 restaurants plans to sell a franchise. They need a unified digital platform with the franchise brand, not Meni.

Solution with Meni#

1. Brought up the storefront under the franchise brand: its own domain (menu.franchise-name.com), logo, colors, font, cover, favicon and QR styling — the guest never sees the platform's name
2. One catalog for the whole network account: a new item is immediately available to any location, while each location picks its categories and items, its own price and its stop list
3. Centralized management: promotions, discounts, new items — pushed to all locations of the network at once
4. Each location sees only its own analytics; the franchisor sees the entire network
5. Automated reporting: revenue, food cost, average check per location

The network is run from one account with several locations and access levels: there is no roll-out of a master menu into separate franchisee accounts. An item's name and description are shared across the network; price and availability are overridden per location. See Multi-location for details.

Result#

• Launching a new franchise location: in 1 day (instead of a week of setup)
• Unified quality standard: 100% of locations with an up-to-date menu
• The franchisor controls the brand, prices, and quality remotely
• Cost of digital infrastructure per location: 5x cheaper than a standalone solution