A travel review platform needed review aggregation across scattered hotel and destination sources worldwide, in every language, after earlier crawling attempts broke down at scale.
Client Overview
A travel review platform set out to build one of the largest hotel and destination review databases on the web, pulling together reviews scattered across countless individual sources into one place. The ambition was global from the start, every country, every language, plus the author profiles and images that came attached to each review, not just the review text alone.
The platform had already tried a few web crawling approaches before coming to PromptCloud, and each one worked until the data started to scale. New sources kept appearing, existing sources kept growing, and a crawling setup that held up at a small scale started breaking down once both the number of sources and the volume of reviews began growing exponentially. Review aggregation at this scale needed a different kind of foundation.
Client Requirements
The platform’s brief to PromptCloud went well beyond a single source list:
- Reviews aggregated from scattered sources covering hotels and destinations worldwide
- Coverage across all countries and all languages, not a single market subset
- Author profiles and images captured alongside the review text itself
- A crawling approach that could keep scaling as sources and volume kept growing
- Regular, ongoing delivery of new data, not a one time historical pull
Challenges
The platform’s earlier crawling attempts were not built for open ended growth. A setup that worked for a fixed list of sources started to strain the moment new sources needed adding or existing ones grew busier, and aggregating reviews across sources this scattered meant constantly reconciling data that arrived in different formats, languages, and structures.
Duplicate data was its own problem. Reviews already collected needed to stay out of future deliveries, otherwise the platform would spend as much effort filtering old data out as it did using new data, defeating the purpose of continuous aggregation in the first place. None of the platform’s earlier solutions had solved this cleanly enough to keep pace with how fast both the source list and the review volume kept growing.
Solutions
PromptCloud rebuilt this around parallel historical and incremental extraction, machine learning driven crawl prioritization, and a site list that could flex as the platform’s own requirements changed.
Historical and Incremental Extraction in Parallel
Rather than treating a full historical pull and ongoing updates as separate projects, PromptCloud ran both in parallel, pulling each source’s full review history while simultaneously capturing new reviews as they were published. That combination is what let the platform build out its historical database and stay current at the same time, rather than finishing one phase before starting the other. Data was de-duplicated before delivery, so only genuinely new records reached the platform, keeping review aggregation from turning into a repeated cleanup exercise.
Machine Learning Driven Adaptive Crawling
Not every source updates at the same pace, so PromptCloud applied machine learning techniques to prioritize crawling, visiting more active pages more often and spending less effort on pages that rarely changed. This adaptive approach is part of what separates a purpose built review aggregation pipeline from a simpler crawler applied evenly across every source regardless of how often it actually changes, a distinction worth understanding when comparing PromptCloud’s approach to other web scraping providers the platform had tried before.
A Site List That Could Grow With the Platform
The list of sources being crawled was never treated as fixed. As the platform’s own requirements changed, sources were added or adjusted dynamically rather than requiring a new project setup each time. This is what let data collection keep pace with a source list that kept expanding, instead of the crawl setup becoming outdated the moment the platform’s own scope grew past what it originally specified.
Delivering at a Scale the Platform Could Use
Over 20 million structured records were delivered within 2 months, giving the platform access to roughly 1 million fresh data points a day. Notifications went out whenever new data was ready, so the platform’s team could import on its own schedule rather than checking manually for updates, freeing that team to focus on other projects instead of babysitting a data feed.
Review Aggregation, Before and After PromptCloud
| Area | Before | After |
| Scaling | Broke down as sources and volume grew | Built to keep scaling as both grew |
| Duplicate handling | Required manual cleanup effort | De-duplicated before delivery |
| Crawl prioritization | Same effort applied to every source | ML driven, prioritizing active pages |
| Cost | Required an in-house crawling team | 37% lower cost than building that team in-house |
Benefits to the Client
Value addition from the project reached 50 times the platform’s spend, driven by the combination of scale, speed, and quality the pipeline delivered. Data quality improved substantially without any added time investment from the platform’s own team, and a 37 percent cost saving came simply from not having to build and staff an in-house crawling team.
Productivity gains freed the data team to work on other projects, and the platform used that freed capacity to expand into new verticals rather than staying anchored to review aggregation alone. Low turnaround time on fresh data also strengthened the platform’s own marketing, since new content was available to promote almost as soon as it was collected.
Review Aggregation Built to Scale Past Where Earlier Attempts Broke Down
A review database this size cannot run on a crawling setup built for a smaller, fixed list of sources. Review aggregation at global scale, every country, every language, every new source that appears, needs a pipeline built to keep growing rather than one that has to be rebuilt every time the scope expands.
That is what changed the outcome here, parallel historical and incremental extraction, de-duplication built in from the start, and crawl prioritization that adapts on its own. Together, that is what turned a database that kept outgrowing its own infrastructure into one built to keep growing indefinitely.
