Travel & Hospitality · Dynamic room pricing
Pricing rooms once a day costs you money every late-demand shift
Rate decisions made at 9am are wrong by 4pm. A demand model re-prices each room type by segment, lead time, and day-of-week signal — pushing updated rates to your PMS and channel manager every hour, or as often as your stack allows.
Banao builds the pricing engine, the booking-data pipeline, and the integration with your existing systems. Revenue managers keep a manual override on every published rate. No rates change without a human deciding to let the model run.
The first call is free · 45 minutes · no obligation
What we build
What a Banao room pricing deployment includes
A pricing model is only as good as the data feeding it and the system it writes to. We build the pipeline and the integration as part of the deliverable.
A demand model trained on your booking history
We train on your own pace data — pickup, cancellation, no-show, and channel mix by date and segment — not an industry-average dataset. Your property's patterns drive the rates.
Intraday rate updates to PMS and channel manager
Updated rates push to your PMS, channel manager, and OTA connections on a schedule matched to your stack's rate-update limits — catching the late-demand swings that a morning price set misses.
Segment and lead-time pricing logic
The model prices by booking window, day-part, rate plan, and guest segment separately — so a last-minute leisure booking and a 90-day-out corporate block sit at rates that make sense for each.
Revenue manager override on every rate
No rate goes live without the revenue manager's say-so. Override controls are built into the dashboard from day one — the model makes a recommendation; the human approves, adjusts, or holds.
A dashboard your team actually opens
Occupancy pace, pickup by segment, rate performance vs. prior periods, and override history in one view — built for the person who carries P&L responsibility, not for the engineer who built the model.
Legacy PMS and channel manager integration
We have wired pricing engines into older and closed property management systems via their export feeds, APIs, and in some cases scheduled database reads. The integration audit happens in week one.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Daily manual rate-setting replaced by an intraday demand model
A multi-property operator was pricing rooms by segment once each morning. Late-checkout demand spikes and weekday-evening softness went unpriced until the next day. Banao trained a pickup model on three years of reservation history and connected it to the group's channel manager — with the revenue team holding final approval on every rate the model suggests.
Dogfooding
We run Banao's own operation on the AI we sell
Banao is a ~300-person engineering company that depends on its own AI products before any client sees them. InterviewGod screens our own hires; Vikaas runs our own demand generation. A system that has to survive our own operation reaches your revenue desk already hardened against real-world noise.
That matters specifically for pricing AI: a model that misbehaves costs money per night, per room, per property. We hold our own systems to exactly that standard.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When dynamic pricing AI isn't the right call
We would rather tell you where the model won't pay off than sell you one that quietly underperforms:
- Too few rooms: a boutique with a small room count and a GM who knows the market can beat an algorithm most weeks. The model earns its keep at scale.
- No booking history: a new property has nothing for a model to learn from. Spend the first season building occupancy data; add AI once there's enough signal to train on.
- Single-channel dependency: if nearly all bookings arrive through one OTA that already manages your rates, building a parallel pricing layer adds cost without meaningful control.
- Unstable comp-set: if the competitive set changes frequently or comp-rate data is unavailable for your market, the model's rate signals are weaker and we will say so in the Sprint.
How we start
How we start — fixed-price, no obligation
A pricing AI project that skips the data audit tends to misprice rooms from day one. We look at your actual booking history before quoting anything.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your PMS export and booking history, model the pickup signal in your data, and return a baseline accuracy estimate, an intraday pricing roadmap, and RevPAR uplift maths — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Data pipeline first: we ingest your reservation feed, clean and join it, and wire the output to your PMS and channel manager. The pricing model trains on your real pace data. Integration and the revenue dashboard are both part of the deliverable.
- 03
Production & continuous learning
Go-live with revenue manager override controls and a pace dashboard. The model retrains weekly on incoming bookings, so seasonal and market shifts are absorbed automatically rather than requiring a manual recalibration each quarter.
FAQ
Frequently asked questions
Our PMS is old or closed. Does that block integration?
No — integration complexity is expected, and we build it. Banao has connected pricing engines to legacy and proprietary PMS via scheduled exports, API polling, and database reads. The week-one data audit establishes the exact path before any build scope is agreed.
How often will the model update our published rates?
As often as your PMS and channel manager support — typically one to twelve times per day. For most OTA connections, hourly updates are standard. We match the refresh cadence to what your distribution stack can absorb without rate-parity conflicts.
Who controls what rates actually get published?
Your revenue manager does. The model produces a rate recommendation and a reason; the manager approves, adjusts, or holds it. Automated publishing is an option only once the team has built confidence in the model over several months — and only if they choose it.
We don't have clean data. Can we still start?
Yes. Most properties have three to five years of PMS exports somewhere, even if nobody has cleaned them. The Discovery Sprint starts with a data audit: we assess what is usable, identify gaps, and tell you whether augmentation or a shorter training window gets you to a workable baseline.
How is this different from a third-party RMS tool?
Off-the-shelf revenue management tools train on market-wide data and optimise occupancy ahead of rate. Banao trains on your own booking history, builds to your revenue targets, and the revenue manager keeps override on every output. You also own the model and the data pipeline — they don't sit in a vendor's cloud.
Get started
Bring your PMS export — we'll find the pricing signal
In 45 minutes we can assess whether your booking history contains enough pickup signal for intraday pricing to move RevPAR, and what the realistic uplift maths look like for your property type.
Book a Discovery Sprint