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