Order Intelligence

Order Intelligence starts from a simple question: what are customers actually ordering, and why? Order data exists in every venue, but it rarely becomes an operational reading capable of guiding menu, staffing and promotions.

The platform organizes orders into complementary views: product performance, category mix, dayparts and trends. The operator sees which items drive revenue, which erode margin and where behaviour changes between lunch and dinner.

Above the analysis sits an AI insight layer that turns numbers into a short narrative: what changed, where to look, which action makes sense. Not a generic chatbot, a briefing that starts from the venue's data.

The mock-first architecture with a deterministic seed and seams toward Supabase and OpenAI makes it possible to work on the experience without depending on the POS. Routes cover dashboard, products, insights and connectors, so the flow is complete and demonstrable.

It is built with Next.js 14, React 18, TypeScript, Tailwind CSS, Framer Motion and Supabase. In the seed the workspace covers 12 venues and 480,000 orders, with a €31.80 average value and a 22.6% attach rate: scale is already part of the product.