Food & Beverage · Kitchen operations automation
The kitchen backs up because routing is still manual
Banao builds kitchen operations automation that routes tickets to stations by live queue depth and item complexity — not the order they arrived — and surfaces to the expediter which ticket is at risk before the table asks.
The system runs against your real POS and KDS data: no synthetic workloads, no demo environment. It integrates with your existing display hardware and works within the ticket vocabulary your kitchen already uses.
The first call is free · 45 minutes · no obligation
What we build
What a kitchen automation deployment covers
Kitchen automation is not a single feature. It is the routing logic, the prediction layer, the KDS integration, and the expediter view — we build and own all of them.
Station workload routing
Tickets are dispatched by current queue depth and item complexity at each station, not first-in-first-out. High-complexity items route to the least-loaded station; simpler builds batch together when throughput allows.
Prep-time prediction by item and station
The model learns how long each item takes at each station during each daypart, so the expediter sees an expected completion time per ticket — a number derived from your actual kitchen history, not a generic estimate.
Rush-window demand forecasting
Order volume is predicted by 30-minute window using POS history and day-of-week patterns, so the kitchen knows a cover peak is 20 minutes away before it arrives — not while it is happening.
KDS integration without hardware replacement
Routing logic pushes into your existing kitchen display system through its API or a lightweight middleware layer. The kitchen team keeps the screens they know; the queue order behind them changes.
At-risk ticket surfacing for the expediter
When a ticket is trending late — station backed up, item mis-routed, ingredient hold — the expediter view flags it with the blocking station and elapsed time, so intervention happens before the guest notices.
Operator override with feedback loop
Kitchen staff can re-route, hold, or escalate any ticket manually. Those interventions feed back into the model so the routing logic learns your kitchen's real constraints, not a generic food-service template.
Receipts
Where kitchen automation is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Ticket routing moved from FIFO to station-load balancing
Manual FIFO queuing during the dinner peak caused consistent station overload on one side of the kitchen while others sat idle. Banao integrated station-load routing with the existing KDS, and the peak throughput pattern shifted within the first fortnight of go-live.
Dogfooding
We run coordination problems before you do
Banao coordinates a ~300-person engineering operation with AI before selling that same capability to clients. Vikaas manages Banao's own demand generation; InterviewGod processes Banao's own hiring queue. Both are coordination and routing problems at their core — the same class of problem as a kitchen under load.
We do not design kitchen automation from the outside. We have operated AI-driven coordination under real operational constraints, and those lessons inform what we build for your kitchen — starting with what breaks when volume spikes.
Runs Banao's own demand-gen coordination pipeline end to end.
Routes and screens Banao's own engineering hire pipeline every week.
The honest version
When kitchen automation is not the right investment
Not every kitchen backlog is a routing problem. We will audit before we build:
- Staffing shortage: if the kitchen is short three cooks, re-routing tickets does not add people. Automation helps a staffed kitchen work better — it does not substitute for the right headcount.
- Unstable menu: if the menu changes weekly in ways that invalidate prep-time history, the prediction layer degrades faster than it earns back. Stable core menus benefit most.
- Legacy KDS with no API: some older display systems have no integration surface. If hardware replacement is the prerequisite, we will tell you the real cost of that before you agree to anything.
How we start
How we start — your POS data first, a model second
We do not quote a routing system before we have seen your actual ticket history and kitchen layout.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your POS ticket data, your current KDS setup, and your station layout. We run a feasibility test on your real peak-hour patterns and hand back a throughput baseline, a routing model sketch, and an integration map — yours to keep. If you proceed, the Sprint fee credits against the build.
- 02
Build
Train the prep-time and routing models on your POS history, integrate with your KDS, wire the at-risk ticket view to the expediter station, and validate against your actual peak periods before go-live.
- 03
Production & continuous learning
Live routing with operator override, a kitchen manager dashboard showing station throughput and ticket age by daypart, and a feedback loop so every manual intervention improves the next shift.
FAQ
Frequently asked questions
Which POS systems do you integrate with?
We have integrated with the major cloud POS systems used across QSR and casual dining — the specific list is settled in week one of the Discovery Sprint once we see your setup. Custom or on-premise POS systems are supported via middleware where the ticket data is accessible.
Will this replace our kitchen display system?
No. The routing logic pushes into your existing KDS through its API. We change what appears on your current screens and in what order — we do not replace the hardware or the display software the kitchen team already knows.
How does the model handle a menu change or new item?
New items and menu changes are fed in as configuration updates. The model uses the closest historical analogues for prep-time estimates until the new item accumulates enough real history, typically within two to three weeks of steady volume.
What does the Discovery Sprint look at for kitchen automation?
We examine your POS ticket history by station, daypart, and item — specifically looking at ticket age, peak congestion patterns, and the gap between your current FIFO queue and what a load-balanced queue would produce. The output is a feasibility report and an ROI estimate built from your own numbers.
How do kitchen staff stay in control?
Staff can override, hold, or re-route any ticket from the expediter view. Every override is logged and fed back into the model. Floor-team adoption — not just system accuracy — is a delivery requirement we own, not an afterthought.
Get started
Bring your busiest peak-hour ticket log to a 45-min call
Show us the hour your kitchen backs up most. In 45 minutes we will tell you whether station-load routing can close that gap and what your POS history can support.
Book a Discovery Sprint