Meni Use Cases

Example scenarios for introducing digital menus and automation in food service. These illustrate setup choices, rather than measured results from named customers. Measure the effect at your location after launch; the starting conditions in these examples are hypothetical.

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

What to measure after launch#

Time from a menu edit to publication; reprinting costs; share of guests using translations.

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 an online payment provider available for the company’s country
5. Shared the link via Instagram, Google Maps, and business cards

What to measure after launch#

Share of direct orders; confirmation time; payment, delivery and customer acquisition costs.

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

What to measure after launch#

Time to update the chain’s catalogue; revenue differences between locations; assortment margins.

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

What to measure after launch#

Room-service order errors; fulfilment time; orders per occupied room.

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

What to measure after launch#

Drink waiting time; additional table orders; staff workload.

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 45 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

What to measure after launch#

Order selection time in each language; requests for explanations; feedback on menu clarity.

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.

What to measure after launch#

Repeat visits; average check; cost of points earned and redeemed.

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.

What to measure after launch#

Overlapping reservations; no-show rate; table occupancy; administrator time.

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

What to measure after launch#

Orders by food-court outlet; fulfilment time; revenue of each participating outlet.

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

What to measure after launch#

Errors in preorder details; confirmation time; cancellations and refunds; workload by pickup date.

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

What to measure after launch#

Peak-time waiting; share of preorders; write-offs; guest feedback.

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.

What to measure after launch#

Recipe costs; actual purchases and write-offs; item margins.

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"

What to measure after launch#

Drink fulfilment time; order-detail errors; share of additional items in an order.

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

What to measure after launch#

Attempts to order unavailable items; timely stop-list and scheduled-menu updates.

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.

What to measure after launch#

New-location setup time; menu freshness across locations; total digital-tool costs.