We grow sales with AI tools for working with guests
The engine builds a personal ranking of dishes from the digital taste profile of the guest — in the app, at the waiter and in campaigns
Try it
Three points of use
Three Food2Mood solutions for the digital menu, service and marketing
Each module solves its own business task, yet they all run on the same Food2Mood core — the guest taste profile and the tagged menu.
Recommendation engine
Personal recommendations
Analyses order history, tags the menu and builds the guest taste profile to return personal recommendations and upsells over API
Waiter assistant
Prompts for the floor staff
Shows the waiter which dishes, drinks and add-ons to offer a particular guest based on their tastes and current order
Taste audiences
Marketing personalisation
Matches dishes and offers against the taste profiles of the customer base and builds ready audiences for further communication
One platform
one taste profile
recommendations at every touchpoint
Modules connect separately and share one core — start with a single module and add the rest later.
Explore the modulesRecommendation engine
The recommendation engine inside your app
Food2Mood analyses the purchase history of guests, tags the menu by taste attributes and builds a digital taste profile of the user. Trained ML models then decide which dishes, add-ons and combinations are most likely to suit a particular guest.
Analyses purchase history
Detects stable preferences, choice patterns and frequent ordering scenarios
Tags the menu and the users
Labels dishes and guests by tastes, ingredients, cuisines, formats and restrictions
Understands pairings and upsells
Finds which dishes, drinks and add-ons go together most often
Uses trained ML models
Builds recommendations from the taste profile, the cart and the order context
Guest data + menu markup + ML models = accurate personal recommendations
Recommendation engineProven in practice
A/B test results in a coffee chain app
to revenue from upsells compared with a conventional recommendation system
Measured over the first week of the test in the app. The comparison was not against having no recommendations, but against the standard ranking logic already in place.
Recommendation engineHow it works
From guest data to a personal recommendation
The app passes Food2Mood a guest id and the current cart; the engine matches them against the taste profile and the tagged menu and returns a personal ranking.
Identification
The app passes a user_id, and Food2Mood finds the taste profile and order history
Taste profile
The system uses purchase history and the preferences already known
Real-time recommendations
The app sends behaviour data, and the engine adjusts the ranking to the current situation
Business settings
The engine respects the rules and priorities set by the restaurant in the admin panel
Balance of interests
The ranking keeps dishes that suit the guest and serve the goals of the venue
The engine runs in two scenarios: it builds a personal ranking by user id, or recommends the next dish given the cart already assembled
Recommendation engineManaging recommendations
Managing and tuning recommendations
The restaurant sets the rules and priorities, and recommendations show the right dishes to the guests they actually suit.
Ideal order
Defines the target order structure the engine aims for when picking add-ons
High-margin dishes
Raises the priority of high-margin items among the relevant recommendations
Food pairings
Defines correct combinations of dishes, drinks and add-ons
Go-list and stop-list
Boosts the items that need to sell and excludes the unavailable or unwanted ones
Food2Mood balances relevance for the guest against the commercial goals of the restaurant
Recommendation engineEffects for the guest and the restaurant
The guest chooses faster, the restaurant steers demand
Food2Mood personalises the ranking inside the digital menu, helps guests find suitable dishes faster and uses order history and the current cart for the next recommendations.
For the guest
- Faster choiceNo need to scan the whole menu by hand
- Personal rankingDishes are ordered with tastes in mind
- Relevant add-onsSuggested items match the order
For the restaurant
- Higher conversionA personal ranking gets guests to the order faster
- Higher average checkRelevant add-ons grow the basket
- Steerable recommendationsBusiness priorities are respected alongside guest interests
The longer the history with a guest, the sharper the personal ranking and the cart recommendations
Waiter assistant
Prompts for the waiter while talking to the guest
Food2Mood helps the waiter during service: a progress bar shows how the order is developing and what else can be offered, while the main screen carries the current tasks that a manager can adjust.
Navigates the order
The progress bar helps the waiter see what can still be offered
Shows the next step
The system surfaces suitable dishes, drinks and add-ons
Tasks for the waiter
The main screen shows current shift tasks — shared or personal
Respects business goals
Priorities are configured by the manager
Food2Mood turns knowledge about the guest into practical prompts for the staff
Waiter assistantHow it works
Deep personalisation as a wow-service tool
Before taking the order the waiter sees the loyalty card and the guest profile, and then serves the guest as if the preferences, habits and restrictions were already known.
Shows the guest profile
Tastes, restrictions and preference history on one screen
Remembers guest habits
Favourite dishes, drinks and service details
Prompts the staff
What to offer, what to avoid and how to elevate service
Creates a wow effect
The guest feels a personal approach from the first contact
Guest profile
Table 24- Likespasta, seafood
- Drinklemonade
- Do not offermushrooms
- Orders oftencaesar
- Preferstable by the window
- Last visit10 days ago
Food2Mood makes personal service a practical wow-service instrument
Waiter assistantEffects for guest, staff and business
Personal service becomes systematic
Food2Mood helps every employee give personal recommendations, respect guest restrictions and use data to grow the average check.
For the guest
- Personal serviceRecommendations account for tastes and context
- Faster choiceNo need to study the menu at length
- Restrictions respectedLess risk of getting an unsuitable dish
For the waiter
- Ready promptsDishes, drinks and add-ons
- Arguments to useEasier to explain why a dish fits
- Faster onboardingNew staff learn the menu quicker
For the business
- Higher average checkRelevant upsells based on guest preferences
- Service standardRecommendation quality depends less on experience
- More loyaltyThe guest gets an offer that genuinely suits them
Taste audiences
Building audiences for precise communication
Food2Mood analyses the menu and order history, builds a digital taste profile of guests and finds the most relevant audience for a particular offer.
Menu markup
Dishes get gastronomic attributes: ingredients, tastes, textures, serving format and consumption scenario
Guest taste profile
Order history turns into a digital profile: favourite ingredients, tastes and behaviour patterns
Audience search
The system matches the offer profile against the customer base and finds the most relevant guests
Taste audiencesHow it works
Every message becomes a personal recommendation
The system analyses the dish, compares it with guest taste profiles, excludes the irrelevant audience and generates messages with different accents.
Choosing the offer
The marketer picks a dish, a new item or a promotion
Dish data
The system pulls the dish card from the tagged menu
Taste analysis
It determines tastes, ingredients, cuisine and dish attributes
Audience search
The system finds the guests the offer suits
Filtering
Guests with a mismatch or restrictions are excluded
Ready segment
The audience and taste segments are handed over for communication
Selection example at the audience search step
- · Likes prawns
- · Often orders soups
- · Asian cuisine
- · Likes spicy food
- · Orders seafood
- · Often picks first courses
- · Likes sweets
- · Orders meat
- · Excludes fish
Targeted communication instead of mass mailings that exhaust the customer base
Taste audiencesEffects for guest, marketing and business
Precise communication for higher relevance
Food2Mood picks the relevant audience, adapts the copy to guest tastes and helps bring guests back with offers that genuinely suit them.
For the guest
- RelevanceGuests receive messages about dishes that match their tastes
- Less advertising noiseThe system leaves out guests the offer does not suit
- Personal accentThe same dish is described differently for different guests
For marketing
- Faster campaign launchOne idea turns into a ready campaign
- Smart audienceThe system finds the audience by taste profile itself
- Personal offersMessages land closer to guest needs and interests
For the business
- More repeat ordersRelevant messages bring guests back more often
- Higher campaign conversionMessages match guest interest more precisely
- The system learnsCampaign reactions return to the profile and improve recommendations
Food2Mood turns CRM into a personal channel of recommendations and repeat sales
How it fits together
Three solutions run
on one taste profile
Digital taste profile
A shared core for all three modules
The profile is fed by events from all three modules and returns recommendations to them — the more touchpoints are connected, the sharper the output in each of them.
CDP
To get more out of your data — connect the CDP tools
Recommendations get sharper as the data about the guest gets fuller. A CDP collects it from every channel into one profile and turns it into personal scenarios.
Data collection points
Collect guest data during service
Guest data
Order history, preferences, taste profile, restrictions
CDP
Merges all the information into one customer profile
Personal scenarios
Audience segmentation, triggered events, personalisation of site and apps
Revenue growth
Higher average check, more frequent visits, higher LTV
Where the data comes from
- POS: orders, receipts, basket composition
- Waiter assistant: recommendations, preferences
- Site and app: menu views, cart
- Delivery: online orders, purchase frequency
- Campaigns: opens, clicks, reactions
- Loyalty programme: bonuses, status
What you can do next
- Segmentation by behaviour and value
- Triggered scenarios based on events
- Templates for SMS, push and email
- Personalisation of the site and the app
- Managing bonuses and statuses
- Analytics of campaign performance
CDPSystem modules
Key modules of the CDP
Unified customer profile
Merges customer data from different sources into a single user card
Audience segmentation
Builds customer groups by shared traits and behaviour models
Campaign manager
Lets you run triggered omnichannel communication scenarios
Loyalty programme
Tiers, bonuses, certificates and deposits instead of scattered discounts
Personalisation
Adapts the site and the app to a particular customer segment
Message templates
An editor for SMS, email and push templates
Pixel
Tracks customer actions on the site and records events
Analytics
Shows how the modules perform and builds reports
Analytics closes the loop: it shows the result of every action and helps improve the next communication
Why Food2Mood
Personalisation that moves revenue
Food2Mood brings the menu and the service together around the guest taste profile, so every contact becomes sharper and commercially useful.
More orders
Guests pick a suitable dish faster, and the restaurant loses fewer orders inside the menu
Higher average check
The system suggests relevant drinks, desserts, sets and priority items
More visits
Personal communication brings guests back with offers that actually suit them
Food2Mood — taste-preference AI infrastructure for restaurants
Menu · Service · Recommendations · Repeat sales
Personal data operator
The company is listed in the operators register; guest data is processed under Russian law
Two registered programs
The recommendation engine and related modules are registered as in-house software
Moscow IT startup register
Food2Mood is listed in the city IT startup register and works with Russian chains
API integration
The engine plugs into your app and does not require replacing the restaurant POS
Access separation
Settings and data are split by role: chain admin, venue manager, waiter
Service data only
What leaves the perimeter is a guest id and menu markup, not personal details
Send a request
Apply to join the programme. We will get in touch, clarify the details and help pick the solutions that fit your organisation.
FAQ
A recommendation system for restaurants and food-service chains. It builds a digital taste profile of the guest, matches it against the tagged menu and suggests what to offer a particular person at a particular moment instead of the same menu for everyone.
Three. The recommendation engine returns personal ranking over API. The waiter module shows prompts to the floor staff. Taste audiences build segments from taste and behaviour patterns for marketing. All three share one core and can be connected separately.
Over API: the system takes a user id and returns an ordered menu — a ranked list of dishes for that guest. It plugs into your existing app, so there is no need to build a separate interface.
No. The engine works on top of your current stack: it receives the menu markup and order data and returns the ranking back to your app or to the waiter assistant.
Priority dishes, exclusions, food pairings, stop-list and go-list, a separate boost for high-margin items and the structure of the ideal order. The engine builds recommendations inside those rules, not against them.
A coffee chain: +5% to upsells and +5% to their share of app revenue in the first week of an A/B test. An author-cuisine restaurant: +10.6% to the average check. A ready-meal service: a pilot with a forecast of +10% to conversion. A federal retailer: a ready-meal pilot with a forecast of +2% to GTV at scale.
The waiter opens the table, sees the guest profile and ready prompts: what to recommend and what to add to the order. A manager can set a task — for the whole shift or for one employee — and track how it is going.
From order history, the current cart and behaviour in the app. This is matched against the menu markup — tastes, ingredients, cuisine and dish attributes — and the personal ranking is assembled from the overlaps.
The audience is collected by taste segments rather than by broad traits: the system picks guests for whom the offer is genuinely relevant and excludes the rest. Targeted communication instead of mass mailings that burn the base.
Send a request — we will discuss the tasks of your venue, pick a suitable module and pilot terms. An A/B test on your own audience is used to measure the effect.