Industries · Travel & Hospitality
AI that runs at the front desk, not in a slide deck
Banao builds and deploys AI across hotels, resorts, and restaurant groups — dynamic room pricing, guest-service automation, demand forecasting, and review analytics — wired into your PMS, booking engine, and POS.
Every system below runs against live booking and property data, not a demo dataset. We hand over deployed systems integrated with your stack, not a deck of recommendations.
CP Plus— computer vision on the cameras already in your lobby and back-of-house.
The first call is free · 45 minutes · no obligation
What we build
What we deploy in travel & hospitality
Each of these has revenue attached — an empty room sold late, a no-show, a one-star review, a shift overstaffed. We start where the number is measurable.
Dynamic room & rate pricing
Demand models that set rates by date, segment, and lead time, pushed straight to your PMS and channel manager. Revenue managers keep a manual override on every rate.
Guest-service automation
Chat and voice agents that handle booking changes, common requests, and front-desk FAQs across WhatsApp, web, and phone — routing anything non-routine to a human with full context.
Booking demand forecasting
Occupancy and covers forecasts by property, segment, and channel that feed pricing, procurement, and rosters instead of last year's spreadsheet.
Review & sentiment analytics
Reviews from OTAs, Google, and survey forms pulled into operational themes per property, so a recurring housekeeping or check-in complaint reaches the GM before the rating drops.
Housekeeping & staff scheduling
Room-turn sequencing and demand-based rostering that match staff to forecast occupancy and check-out load — fewer idle shifts, fewer guests waiting on a clean room.
Operations document intelligence
Group RFPs, supplier contracts, and SOPs pulled out of PDFs and email into a searchable, chat-queryable base for sales and front-office teams.
Receipts
Deployed, with names attached
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
Computer vision on the camera infrastructure already on site
Banao adds an AI layer to existing CP Plus CCTV in lobbies, entrances, and back-of-house — occupancy counts, queue alerts at the front desk, and safety-compliance checks — using the cameras a property already owns rather than installing new hardware.
Manual rate changes replaced by a demand-driven pricing model
A multi-property operator priced rooms by hand once a day, missing late demand swings. Banao trained a demand model on its booking history and pace data and wired the output into its PMS and channel manager, with the revenue team holding final say on every published rate.
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 who depends on it daily. A guest-service agent or forecast that has to hold up inside our own operation reaches your property already tested against real traffic.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When hospitality 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 revenue and operations heads take our second call.
- Tiny inventory: a single boutique property with a handful of rooms is priced fine by a manager who knows the market. A model adds cost, not margin.
- No booking history: a brand-new property gives a forecast nothing to learn from. Start with sensible rules; add AI once a season of data has accrued.
- One captive channel: if nearly every booking comes through a single OTA that already sets your price, the pricing win is small and we will say so.
How we start
How we start — fixed-price, low risk
You have been pitched a pricing tool or a chatbot by other vendors already. 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 pricing, guest service, and operations, 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 booking-data pipeline as a deliverable and integrate with your PMS, channel manager, booking engine, and POS — legacy systems included.
- 03
Production & continuous learning
Deployment with staff override and a dashboard your front-office and revenue teams actually open, plus change management for the property. The model keeps improving as each week's bookings come in.
FAQ
Frequently asked questions
Our PMS is old or proprietary. Does that rule us out?
No — integration is the work, and we expect it. Banao has wired AI into legacy and closed PMS, channel managers, and POS via their APIs, exports, or a database read. We run an integration audit in week one so there are no surprises at go-live.
We don't have clean booking data. Can we still start?
Yes. Nobody has clean data. We need some history, not perfect history. The first two weeks of any engagement is data engineering, and the booking-data pipeline is part of the deliverable, not a prerequisite.
We tried a pricing tool that underpriced our rooms. Why is this different?
Off-the-shelf tools optimise for occupancy and quietly discount your inventory. We build the model around your revenue targets, train it on your own pace data, and leave the revenue team a manual override on every published rate.
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 our properties?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout across properties. 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 properties
Bring your biggest source of lost revenue — empty rooms, no-shows, slow front desk, or falling ratings. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a Discovery Sprint