A dynamic pricing engine for hotels, ecommerce SKUs, classes, freight lanes. Demand, capacity, competitor, inventory — all weighted, all live. Recommendations at hourly cadence, with override and audit trail. Powered by your data, not someone else's algorithm.
Demand, capacity, competitor — the right price, the right channel, every hour.
Live signals from your booking engine, your storefront, your traffic. Demand curve modelled hourly. Anomalies (event-driven spikes, weather) flagged.
Inventory remaining, time-to-event, sell-through curve vs forecast. The model knows when you're behind or ahead and prices accordingly.
Competitor prices scraped daily for like-for-like SKUs / room types / lanes. Position-vs-market displayed; never automatic, always informative.
Each recommended price comes with confidence score and the dominant signal. Operator approves, edits, or overrides. The algorithm learns from overrides.
On approve, prices push to Booking.com, Expedia, your direct channel, OTA APIs. SKU updates push to Shopify, Amazon, eBay. One number, every shelf.
Floor price per SKU / room / lane. Recommended prices below floor are blocked, with override allowed and logged. The model can be aggressive, but never reckless.
A four-property boutique hotel group set prices manually on Sundays. RevPAR ran 14% behind comp set. We shipped a dynamic-pricing engine integrated with Mews and SiteMinder. Six months in: RevPAR closed the gap and went 6 points ahead, owner Sundays back, operator time on pricing went from 4 hrs/week to 20 mins of approvals.
How we measure: RevPAR measured against STR comp set monthly, owner time tracked via self-report, override rate tracked via app to validate model trust.
Most-relevant verticals — but the same shape works for adjacent ones.
We wire your signals, your competitors, your channels — ship in 4–6 weeks.