Menu Optimizer

Menu Optimizer starts from a problem restaurant menus rarely address with data: every dish consumes margin, kitchen time and the guest's mental space, but decisions are often made out of habit or taste of the moment.

The platform builds a menu-engineering matrix crossing margin and popularity, with an insight rail to switch lenses — margin, volume, price, mix — and a decision-oriented performance table. Not a report to read, a surface to act on.

Recommendations translate data into a move: raise the price, revise the recipe, reposition the dish on the menu, reduce the portion, remove an item that takes space without contributing. Every suggestion has an expected impact and a reason.

The architecture is mock-first with a Supabase adapter and a deterministic dataset of dishes, segments and recommendations. This makes it possible to validate the menu-engineering logic before connecting POS, costs and real sales.

It is built with Next.js 14, React 18, TypeScript, Tailwind CSS, Framer Motion and Supabase. In the seed, gross margin is 71.8%, average popularity is 63% and margin leakage is €1.9k: the menu stops being a list and becomes a portfolio.