A popular Indian travel portal needed hotel pricing intelligence at scale, matching competitor rates across hundreds of thousands of listings, some updated as often as twice a day.
Client Overview
A popular online travel portal in India wanted to build its own internal system for matching hotel prices against competitors, using pricing data pulled directly from the sites it was competing with. The portal’s target sites carried hundreds of thousands of hotel listings apiece, built on complex, dynamic page structures that were never going to yield to a simple script.
The portal had no in-house team equipped to take this on, and hotel pricing intelligence at this scale needed more than occasional scripts, it needed a fully managed service willing to take end-to-end ownership of the extraction itself. On top of the scale problem, some of the portal’s target sites needed checking as often as twice a day, a frequency that only compounds how resource intensive large scale hotel price extraction really is.
Client Requirements
The portal’s brief to PromptCloud covered scale, frequency, and delivery all at once:
- Pricing data extracted from complex, dynamic hotel booking sites with hundreds of thousands of listings
- A fully managed service, since the portal had no in-house technical capability for this
- Crawl frequency as high as twice a day on certain target sites
- Delivery in JSON format, accessible through the PromptCloud API
- Full ownership of the technical process, end to end
Challenges
Hotel booking portals do not make scraping easy. Complex, dynamic coding elements meant a crawler had to render and interpret pages the way a browser would, not just pull raw HTML, and doing that reliably across hundreds of thousands of listings per site demanded real infrastructure, not a handful of scripts running on a laptop.
Frequency added its own pressure. Extracting a site twice a day is a different resourcing problem than extracting it once a week, since the crawl has to complete, validate, and deliver within a much shorter window every single time. With three separate target sites, each needing its own frequency, hotel pricing intelligence here meant managing three different operating rhythms at once, not one crawl running on a single schedule.
Solutions
PromptCloud took full ownership of the extraction as a managed site crawling engagement, building around the portal’s exact frequency and delivery requirements from the start.
Infrastructure Built for Hundreds of Thousands of Listings
Handling target sites with complex, dynamic page structures and listings in the hundreds of thousands meant building crawlers capable of rendering pages accurately at real scale, not just fetching static HTML. This is the kind of infrastructure question worth weighing when comparing PromptCloud’s approach to other web scraping providers, since hotel pricing intelligence at this scale lives or dies on whether the underlying crawling infrastructure can actually hold up, not just whether it works in a small test run.
Three Sites, Three Different Crawl Frequencies
Rather than forcing every target site onto the same schedule, PromptCloud ran the three sites at the frequencies the portal actually needed, twice daily for the most time sensitive site, daily for another, and fortnightly for the third. Managing three independent cadences meant each site’s crawl could be tuned to how often its prices genuinely changed, rather than over-crawling a slow moving site or under-crawling one that updated by the hour.
Full Ownership, Delivered Through the API
Since the portal had no in-house technical capability for this, PromptCloud took complete ownership of the crawling process end to end, delivering extracted data in JSON format through the PromptCloud API rather than handing back a raw file the portal’s team would need to process further. That end-to-end ownership is what let a portal without its own scraping expertise still run a hotel pricing intelligence system built on data it never had to touch directly.
Live in Five Days
The crawler setup for all three target sites was completed in just five days, and the first web crawl alone delivered about 2.5 million records to the portal, solving its hotel price matching problem almost as soon as the system went live. That speed mattered as much as the scale, since a portal with no internal scraping capability needed to see results quickly to justify handing the entire process over to a managed partner.
Hotel Price Matching, Before and After PromptCloud
| Area | Before | After |
| Technical capability | No in-house scraping expertise | Fully managed, end-to-end ownership by PromptCloud |
| Crawl frequency | Single generic schedule assumed | Three sites, three tailored frequencies |
| Data delivery | Raw files needing further processing | JSON via PromptCloud API, ready to use |
| Time to value | Unknown, no existing system in place | Live in 5 days, 2.5M records on the first crawl |
Benefits to the Client
The portal solved its hotel price matching problem almost immediately, the first crawl alone delivered about 2.5 million records, giving its pricing system real volume to work from on day one. Because PromptCloud took full ownership of the extraction, the portal never had to build the in-house technical capability it lacked, freeing its own team to focus on using the data rather than collecting it.
Running three target sites on three separate frequencies, twice daily, daily, and fortnightly, meant each site was crawled at the pace its prices actually changed, rather than over-crawling a slow moving site or missing changes on a fast moving one. Setup took only five days from start to finish, and data arrived ready to use in JSON format through the PromptCloud API, with no extra processing needed before the portal’s own systems could consume it.
Hotel Pricing Intelligence That Matches the Portal’s Own Pace, Not a Generic Schedule
Matching hotel prices across hundreds of thousands of listings on sites that update on their own timelines is not a problem a single crawl schedule can solve. Hotel pricing intelligence only works if the extraction respects how often each source site actually changes, twice a day for one, daily for another, fortnightly for a third.
That is what turned three differently paced target sites into one dependable price matching system, live within five days and delivering millions of records from the very first run, without the portal ever needing its own scraping team to make it happen.
