Personal food recommendations for taste, mood and context
Personal food recommendations for taste, mood and context
+10.6%
+5%
+4%
+2%
+10.6%
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 modulesHow 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.
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
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.