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 (12 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 a bonus program: 5% cashback on every order
2. Tiers: Bronze (0₾) → Silver (500₾) → Gold (2000₾) → Platinum (5000₾)
3. Each tier gives higher cashback (5% → 7% → 10% → 15%)
4. Birthday promo codes: 20% discount (automatic campaign)
5. Referral program: bring a friend → both get 10₾ in bonuses

Result

• Guest retention: from 20% to 45% in 4 months
• Average check of returning guests: +30% vs new guests
• Referral program attracted 120 new guests in the first month
• 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. SMS reminder to the guest 2 hours before the visit
5. No-show tracking: after 3 no-shows — restriction on online booking

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. You can order from different outlets in one check
3. Each outlet receives its part of the order on its own KDS screen
4. One payment → automatic revenue split between outlets
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. 50% online prepayment via Stripe
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
• 50% prepayment → 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. Current load display: 🟢 free / 🟡 moderate / 🔴 line
4. Student card balance is linked to the Meni 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. Real-time food cost monitoring (target: 25–30%)
5. Alerts when food cost > 35% for specific items

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: "Your Latte #42 is ready"
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. Notification to the chef when an ingredient balance is < 5 portions

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. Connected whitelabel: logo, colors, franchise domain (menu.franchise-name.com)
2. Master menu template: franchisees get a base menu + can add local items
3. Centralized management: promotions, discounts, new items are rolled out to all locations
4. Each location sees only its own analytics; the franchisor sees the entire network
5. Automated reporting: revenue, food cost, average check per location

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