Travel & Hospitality · Housekeeping optimization
Room turns don't slip at checkout — they slip in the queue
Banao builds AI-driven housekeeping scheduling that assigns rooms to attendants in real time, based on checkout pattern, priority tier, and live staff position — and pushes updates directly to your PMS.
The result is fewer room-not-ready complaints at peak check-in, a tighter match between staffing hours and actual occupancy, and a maintenance queue that housekeepers can clear by end of shift.
The first call is free · 45 minutes · no obligation
What we build
What a housekeeping AI deployment includes
The scheduling problem and the data problem are inseparable. We address both, not just the model.
Dynamic room assignment
Assignment is calculated from live checkout signals, priority tier (VIP arrival, early check-in request), attendant location, and room type — updated every few minutes, not at shift start.
Staff workload forecasting
Occupancy and checkout patterns by date and segment drive staffing recommendations the day before and the morning of — so supervisors arrive with a rostered team that matches actual workload, not last month's average.
Preventive maintenance queue
Defects flagged during room inspection — broken fittings, stained linen, HVAC faults — route automatically to the right trade with priority tied to the room's next arrival time.
Supervisor floor dashboard
A live view showing room status, attendant position, and queue depth by section — so supervisors redistribute rooms mid-shift rather than finding the backlog at 2 pm.
PMS write-back integration
Room status updates write back to your PMS in real time, so the front desk sees available rooms as they clear — not on a manual radio call from the floor.
Supply consumption tracking
Linen, amenity, and cleaning product usage by floor and section, flagged when consumption diverges from the expected rate per occupied room — an early signal for pilferage or process drift.
Dogfooding
We run operational AI on our own company before yours
Banao is a ~300-person engineering firm that operates on its own AI products. InterviewGod screens our own engineering hires every week. Vikaas runs our own demand generation pipeline end to end.
When we build a scheduling or operations model, it has already had to survive the constraints of a real operation — our own. That is a different standard from a proof-of-concept built against a demo dataset.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When AI housekeeping scheduling is the wrong fit
Not every property has the preconditions for scheduling AI to outperform a good supervisor. We will tell you before you build:
- Small properties: below 80–100 keys, a well-trained supervisor with a whiteboard is cheaper and faster than a scheduling system. We will say so.
- PMS data quality: if your PMS checkout timestamps are unreliable or rooms are manually blocked for hours after checkout, the model's signals are corrupted before the first assignment. We audit this in Discovery.
- Fixed-shift labour contracts: if your housekeeping contracts lock headcount regardless of occupancy, dynamic scheduling reduces queue depth but won't move the payroll number. Know the ceiling before you build.
How we start
How we start — audit before we build
We don't quote a scheduling system off a description of your property. We look at your actual PMS data and shift patterns first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We analyse a sample of your historical PMS data — checkout times, room-clear times, staffing logs — map where the gap between checkout and available is largest, and hand back a baseline feasibility estimate and ROI case. Yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
We build the assignment model, staff workload forecaster, and supervisor dashboard, integrated with your PMS and communication layer — app, radio dispatch, or in-room tablet. Data pipeline and integration are part of the deliverable.
- 03
Production & iteration
Go-live with supervisor training, a floor-team change programme, and a 30-day close-support window. Model performance is reviewed against actual turn times weekly in the first month.
FAQ
Frequently asked questions
Which PMS systems do you integrate with?
Banao has integrated with Opera, Mews, Cloudbeds, and Protel, and can build adapters for other systems where a suitable API or data export exists. The Discovery Sprint includes a PMS connectivity audit.
How long does a typical deployment take?
The Discovery Sprint is two weeks. Build typically runs eight to twelve weeks depending on PMS complexity and the number of properties in scope. A single-property pilot can be in production in under twelve weeks from Sprint start.
Can the model handle multi-property or branded-residence portfolios?
Yes. The model is parameterised per property — turn-time targets, room types, and staff ratios differ by site. Multi-property rollout starts with a single pilot property so the pattern is proven before it is replicated.
What happens when a room is flagged for a maintenance fault?
The defect routes to a maintenance queue with priority set by the room's next arrival. If the fault blocks the room for sale, the front desk sees the block in the PMS in real time rather than on the next manual status call.
Do attendants need a new device?
No new hardware is required by default. The system delivers assignments and status updates via a mobile web app that runs on any smartphone — or integrates with your existing housekeeping app where one is already in use.
Get started
Bring us your peak-day checkout data
In 45 minutes we will map where your turn-time gap is largest and tell you whether scheduling AI will close it — or whether the constraint is somewhere else in the operation.
Book a Discovery Sprint