Dashboard demo · Food service, hospitality, QSR · Build in 5–8 weeks
Restaurant Demand Shaping
Flatten the rush. Boost kitchen utilization with AI-timed offers.
The Restaurant Demand Shaping demo shows what an AI-driven pricing nudge does to a typical restaurant's hourly demand curve. Start with the real shape — a sharp 7 PM peak that overwhelms the kitchen — and move the sliders to simulate a targeted discount window. Watch the curve flatten, kitchen utilization climb, and revenue hold or rise. Tied visually to the Gene's Grinders case study where we turned a manual weekly ops process into a hands-off system.
Peak reduction
20.8%
Kitchen utilization
100% → 63%
Revenue delta
-2%
Model is directional, not a live forecast. Production deployments fit this against your actual POS history (Toast, Square, Clover) and refine with every offer cycle. Tied to the Gene's Grinders case study.
Preview runs on canned data · book a call to see it on your own
What this shows
The capabilities demonstrated.
- ✓ Interactive hourly demand curve (SVG, no chart library)
- ✓ Two sliders: discount window start time, discount percentage
- ✓ Live readout: peak reduction, kitchen utilization, revenue delta
- ✓ Before/after annotation so the impact is obvious at a glance
- ✓ Works on mobile; no data or account required
How we'd build this for you
4 steps. Yours, not a template.
Model
We ingest your POS history (Toast, Square, Clover) and build a demand model per daypart, day-of-week, and menu category. Weather and local events are overlayed where they matter.
Simulate
A lightweight simulator predicts what a discount, a happy hour, or a bundle would do to demand. You test pricing ideas in the dashboard before pushing them live.
Push
Approved offers push to your POS, your app, and your email/SMS channels. We do not touch pricing without a human approval step — revenue is too important.
Learn
Every offer cycle improves the model. After 8–12 weeks you have a house view of your own elasticity, and the dashboard starts recommending offers proactively.
Stack
Tools behind this demo.
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Case study · Food & Hospitality
Gene's Grinders
Eliminated peak-hour order bottlenecks.
Read case study →
FAQ
Restaurant Demand Shaping: common questions
- Does AI really change when people eat dinner?
- It changes when a meaningful slice of them eat dinner. You will not flatten a peak to zero — people still want dinner at 7 — but shifting 15–25% of guests into the 5–6 PM or 8–9 PM windows is routine with a targeted discount. The math matters: filling your kitchen's idle minutes is almost pure margin.
- Won't discounting just cannibalize full-price sales?
- Sometimes. The model is designed to predict exactly this and refuses to recommend offers that are net-negative. In practice, the right discount attracts price-sensitive new traffic (off-peak fillers) without pulling your core 7 PM crowd. We track cannibalization as a first-class metric, not as an afterthought.
- What POS systems do you integrate with?
- Toast, Square, Clover, and TouchBistro have clean APIs we integrate directly. Aloha, Micros, and older systems usually need a nightly export or a middleware bridge. If your POS is weird, we scope integration separately — but we have yet to hit one we could not work with.
- Do I need to change my menu?
- No. The first-pass recommendations are timing-based (when to discount what you already sell). Menu-level recommendations — bundling low-margin items with high-margin ones, retiring dogs — come later once we have enough data to stand behind them. We never push a menu change we cannot defend with numbers.
Your turn
Want this demo running on your data?
Free 30-minute discovery call. We scope the build, confirm ROI, and ship a fixed-fee quote — no surprises.