A prominent Indian budget hotel chain needed hotel price data twice a day, in INR, across major OTAs, to match its own prices and identify expansion opportunities.
Client Overview
A prominent budget hotel chain in India needed to update its own room prices twice a day, which meant it needed hotel price data on the same twice-daily rhythm, both for its own listings and for competitors selling rooms in the same markets. Missing a price update cycle meant competing on stale numbers in a category where budget travelers compare prices closely.
The data also had to solve a second problem beyond pricing. The chain wanted to spot new hotels appearing across the country and use that visibility to expand its own network strategically, rather than relying on word of mouth or manual market research. Getting that data reliably enough to serve both purposes, twice a day, every day, meant the chain needed a partner rather than a script.
Client Requirements
The chain’s brief to PromptCloud was detailed down to the hour:
- Twice-daily pricing updates for both the chain’s own listings and named competitors
- Check-in and check-out dates specified by the chain for every crawl
- Data delivered in a predetermined field order, uploaded to a shared file server
- Same-day delivery, completed by 23:59 every time
- All prices converted to INR regardless of the source site’s original currency
- Inventory crawls twice a month to catch new hotels entering the market
Challenges
A twice-daily deadline leaves very little room for error. Every crawl had to pull fresh pricing data from major Indian OTAs, convert prices from whatever currency the source server used into INR, and land in the client’s predetermined field order, all before 23:59 hours on the same day, every single day.
Currency was its own complication, since crawling from servers located around the world meant prices arrived in a mix of currencies that all needed converting accurately before delivery. On top of that, dozens of OTA sites had to be crawled without overloading their servers or triggering blocks, a balance that gets harder to hold the more sites and the more frequently they are crawled, especially against a fixed same-day deadline that could not slip.
Solutions
PromptCloud built the pipeline around the client’s file-sharing workflow, the 23:59 deadline, and the currency conversion requirement all at once, rather than treating any of them as an afterthought.
A Crawl Built Around the Client’s Own File Server
Rather than pushing data directly, the crawlers were built to check the client’s shared file server at scheduled times, once in the morning and once in the afternoon, picking up new files only when they were actually available. That meant the client controlled exactly when new pricing data got pulled by uploading files on its own schedule, rather than PromptCloud dictating a rigid delivery window the client had to work around.
Currency Conversion Built Into the Crawl Itself
Since target sites were crawled from servers located around the world, prices came back in a range of currencies rather than a single one. PromptCloud’s crawlers were built to detect the source currency automatically and convert it to INR before delivery, so the client never had to do that conversion work itself or worry about a mismatched currency slipping through into its pricing system.
Meeting a Same-Day Deadline at Real Scale
Delivering hotel price data by 23:59 every day, across major OTAs generating close to 30 million records a month, meant building in extra scripts and resources specifically to guarantee the deadline held regardless of daily volume swings. It is worth comparing what this actually takes against other web scraping tools built for smaller, less time sensitive jobs, since a same-day deadline at this scale is a very different engineering problem than a weekly or monthly crawl.
Crawling Politely at Scale
Pulling hundreds of thousands of records per site per day meant the crawlers had to pace themselves deliberately, avoiding the kind of aggressive request patterns that overload a target server or get an IP blocked outright. Getting this balance right was what let PromptCloud keep collecting pricing data from the same OTAs day after day without the sources pushing back.
Hotel Price Data Delivery, Before and After PromptCloud
| Area | Before | After |
| Price updates | Manual, hard to match a twice daily cycle | Twice daily crawls, delivered by 23:59 |
| Currency handling | Would require manual conversion per source | Automatic detection and conversion to INR |
| New hotel discovery | Relied on manual market research | Identified through bi-monthly inventory crawls |
| Server load | Risk of blocks crawling many OTAs at once | Paced crawling to avoid overload or blocks |
Benefits to the Client
The chain got regular, flexible access to hotel price data without needing to check in constantly, since the crawlers only pulled new files when the client actually uploaded them. Forming a dedicated team on the client’s side to work with this data, rather than building and running its own crawling infrastructure, brought a 23 percent cost saving.
Bi-monthly inventory crawls gave the chain a fortnightly view of new hotels entering the market, supporting its strategic expansion across the country. Low turnaround time meant the data was useful almost as soon as it arrived, and once past the initial onboarding period, the entire process ran automatically, with the support team notified directly if anything needed attention.
Hotel Price Data That Actually Arrives Before the Deadline
A twice-daily pricing cycle only works if the hotel price data behind it shows up on time, every time, in the right currency, in the right format. Missing that window even occasionally defeats the purpose of pricing twice a day in the first place.
What made this reliable was building the 23:59 deadline, the currency conversion, and the client’s own file-sharing workflow into the crawl itself, rather than bolting them on afterward. That is what turned a demanding same-day delivery requirement into a routine the chain could actually plan its pricing around.
