Notes from operating hotel and flight scrapers in production. Each guide covers one problem that quietly corrupts travel price data, why it happens, and what to do about it — written to be useful whether or not you ever call our API.
Name-and-city matching quietly pairs the wrong hotels across OTAs. Why it fails, what a wrong match costs, and how to key hotels on a Google Place ID.
Most wrong hotel prices from AI agents are data problems, not hallucinations. The six failure modes, and how to design a price tool that avoids them.
Taxes, mandatory fees, occupancy, room type and stay length make two hotel prices incomparable. What to normalise first, with US and India tax rules.
Collecting hotel prices from Indian OTAs: MakeMyTrip group brands, the GST threshold, Indian number formatting and point of sale. What to handle, and how.
If you want parameters and response schemas rather than background, the reference documentation is the faster route:
Multi-OTA price comparison via Google Hotels, per-room rates from Agoda, Booking.com, MakeMyTrip and Expedia, and Rate Shop for many hotels across many dates.
Fare data from Google Flights and MakeMyTrip.
How the same sources are applied in travel & hospitality, by AI agents, and in e-commerce & pricing.