Industries · Food & Beverage
AI that runs on the line and in the kitchen, not in a slide deck
Banao builds and deploys AI across food and beverage producers, QSR chains, and restaurant groups — production-line vision, demand forecasting, kitchen automation, and cold-chain monitoring — wired into your lines, POS, and ERP.
Every system below runs against live production and order data, not a demo set. We hand over deployed systems integrated with your stack, not a deck of recommendations.
Swiggy— order-volume and demand forecasting built on live data across thousands of kitchens.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in food & beverage
Each of these has margin attached — a batch scrapped, a rush mispredicted, a freezer that drifted out of range overnight. We start where the number is measurable.
Production-line quality vision
Custom defect and fill models on line cameras, integrated with your PLCs and reject hardware. Real-time checks on seal integrity, label placement, fill level, and foreign matter across bottling, packing, and processing lines.
Menu & demand forecasting
Demand forecasts by item, outlet, daypart, and channel that feed procurement, prep lists, and rosters — so kitchens stop cooking to last week's guess and waste falls before the bin does.
Kitchen operations automation
Ticket routing and station load-balancing driven by live order flow, plus voice and chat agents that take routine orders and reservations and pass anything unusual to staff with full context.
Cold-chain compliance monitoring
Sensor and time-series models over fridge, freezer, and transit data that flag a temperature excursion before stock spoils, with an audit trail your QA and food-safety team can hand to a regulator.
QSR order automation
Drive-through, kiosk, and app order capture with voice and vision, upsell prompts trained on your own basket data, and accuracy checks that catch a wrong build before it leaves the pass.
Waste & yield analytics
Models over production, sales, and spoilage data that pinpoint where yield leaks — over-prep, trim loss, expiry write-offs — and cost each source so the kitchen knows what to fix first.
Receipts
Deployed, with names attached
Metrics shown dotted (··) are still being finalised in our case-study metrics pack. These deployments are live — we don't publish a number until it has been verified.
Order demand forecast on live data across thousands of kitchens
One of India's largest food-delivery platforms needed demand it could prep and staff against, not a daily guess. Banao worked on forecasting order volume by outlet, item, and daypart from live order data, so kitchens and procurement plan to the demand that is actually coming.
Line and kitchen vision on cameras already installed
Banao adds an AI layer to existing CP Plus cameras on packing lines and in back-of-house kitchens — checking PPE and hygiene compliance, flagging spills, and logging line stoppages — using hardware a site already runs rather than fitting new rigs.
Dogfooding
We run our own company on the AI we sell
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation.
That is the difference between a vendor who has read about production AI and one that runs on it daily. A forecast or a vision model that has to hold up inside our own operation reaches your line already tested against real load.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When food & beverage AI doesn't earn its keep
Most AI vendors will sell you a model regardless. We would rather tell you when not to build — it is why plant and operations heads take our second call.
- Low line volume: a single low-throughput line is inspected fine by a trained operator. A vision pipeline adds cost before it adds margin, and we will say so.
- Stable, simple menu: if your range rarely changes and demand barely moves, a planner with a spreadsheet forecasts it well enough. A model earns its keep when variety and volatility are real.
- No sensors or logs: if a process has no camera, probe, or record at all, week one is instrumentation, not modelling. We scope that honestly before anyone signs.
How we start
How we start — fixed-price, low risk
You have been pitched AI by vendors who never set foot in a kitchen or on a line. We start by proving the cost of the problem, not by quoting a build.
- 01
AI Discovery Sprint
2 weeks · fixed price
On-site if needed. You walk out with a prioritised list of AI opportunities across production, demand, and food safety, baseline ROI maths, and a go/no-go per opportunity — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data engineering first, then the model. We build the data pipeline as a deliverable and integrate with your PLCs, POS, cold-chain sensors, and ERP — older kit included.
- 03
Production & continuous learning
Deployment with operator override and a dashboard your line and kitchen teams actually open, plus change management for the floor. The model keeps improving as each shift's data comes in.
FAQ
Frequently asked questions
Our lines and kitchens run old or mixed equipment. Does that rule us out?
No — integration is the work, and we expect it. Banao has wired AI into legacy PLCs, analog probes, and closed POS via their APIs, exports, or a database read. We run an integration audit in week one so nothing surprises us at go-live.
We don't have clean production or sales data. Can we still start?
Yes. Nobody has clean data. We need some data, not perfect data. The first two weeks of any engagement is data engineering, and the cleaning pipeline is part of the deliverable, not a prerequisite.
We tried a forecasting tool and the kitchen ignored it. Why is this different?
Most food & beverage AI dies on floor adoption — the number looks right, the team doesn't trust it. Our delivery includes change management for line and kitchen staff as a non-negotiable deliverable, and operators keep an override on every call the model makes.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the ROI model whether or not you continue. Worst case you have a free assessment; best case you have your board business case.
How fast can a system reach the line?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week production rollout. Banao's ~300-engineer bench means delivery starts in weeks, not the months a local hire would take.
Get started
Find out where AI actually pays off across your lines and kitchens
Bring your biggest source of spoilage, waste, mispredicted demand, or manual checking. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a Discovery Sprint