A mobile-centric marketplace for borrowing and lending needed mobile app scraping across 10 to 20 sites a month, precise down to latitude and longitude, to build its own listings and car-sharing database.
Client Overview
A neighborhood-based marketplace built for borrowing and lending goods and services, mobile-centric by design, needed a steady stream of listings to keep its own database populated and current. Rather than relying only on what its own users posted, the marketplace wanted to aggregate content from other sites, building out both a general items-and-services database and a separate one for car-sharing listings.
Because the marketplace operated at a neighborhood level, generic city-wide or national listings would not do. Every listing needed to carry specific geographic detail down to latitude and longitude, so the app could actually show users what was available near them rather than somewhere vaguely in the same metro area. Mobile app scraping at this level of geographic precision needed a partner who could handle both the scale and the specificity at once.
Client Requirements
The marketplace’s brief to PromptCloud covered scope, detail, and precision together:
- Crawling 10 to 20 sites per month to source new listings
- A separate database build specifically for car-sharing services
- Core listing fields: name, photo, owner name, location, and price
- Location data precise to latitude and longitude, not just a city or zip code
- Specific geographic targeting matched to the marketplace’s neighborhood level focus
Challenges
Aggregating listings for a mobile-centric marketplace is a different problem than a single large scrape. Ten to twenty sites a month meant an ongoing, recurring process rather than a one-time pull, and each source needed to be crawled in a way that kept the data flowing without disrupting the sites being crawled.
Geographic precision added its own difficulty. Getting latitude and longitude level detail for every listing meant the crawl could not stop at a general address or neighborhood name, it had to resolve location down to coordinates the marketplace’s own app could plot directly. Doing this consistently across two different data verticals, general items and services alongside car-sharing, meant managing two related but distinct data structures within the same ongoing engagement.
Solutions
PromptCloud approached this as part of its regular site-specific crawling work, tuning the crawl behavior itself to match what each source site could handle.
Slow Crawling for Reliable Results
Rather than pulling data as fast as possible, PromptCloud ran slow crawls across the target sites, deliberately pacing requests to get complete, reliable data for each required field. Mobile app scraping and site aggregation at this scale depends on the sources staying accessible over time, and a slower, steadier crawl pattern is what keeps the data flowing smoothly for this case rather than risking disruption to the sites being crawled. That patience is a deliberate tradeoff worth understanding when comparing PromptCloud’s approach to other web scraping tools built around raw crawl speed rather than long-term source reliability.
Two Data Verticals, One Consistent Process
General marketplace listings, items and services available to borrow or lend, were crawled alongside a separate database built for car-sharing services, each with its own relevant fields but built through the same site-specific crawling process. Keeping both verticals running through one consistent pipeline meant the marketplace did not need two separate vendor relationships or two different data structures to reconcile, just one ongoing process covering both sides of its own platform.
Geographic Precision Down to Coordinates
Every listing needed location data precise enough to plot on a map at the neighborhood level, so crawls captured latitude and longitude alongside the core fields, name, photo, owner name, and price. Specific geographic targeting meant the crawl was not just pulling whatever location data a source happened to expose, it was resolving that detail down to the coordinate level the marketplace’s own app depended on to show users what was actually nearby rather than a rough approximation.
A Continuous Stream, Not a One-Time Pull
Crawling 10 to 20 sites every month built a continuous stream of fresh listings rather than a static export that aged the moment it was delivered. That ongoing cadence is what let the marketplace keep aggregating content from other sites on a rolling basis, adding to its own database steadily instead of refreshing it in occasional, disconnected batches.
Marketplace Data Aggregation, Before and After PromptCloud
| Area | Before | After |
| Listing sourcing | Limited to what users posted directly | Aggregated monthly from 10 to 20 external sites |
| Location detail | General address or area level | Latitude and longitude precision |
| Data verticals | Single, undifferentiated listings | General marketplace plus a separate car-sharing build |
| Crawl pace | Not specified, risk of site disruption | Deliberately slow, reliable field-level data |
Benefits to the Client
The marketplace gained a continuous stream of listings from outside its own user base, aggregated from 10 to 20 sites every month rather than depending solely on what members posted themselves. That aggregation work, sourcing, crawling, and structuring the data, sat entirely with PromptCloud, so the marketplace’s own team never had to process or clean the incoming listings before using them.
Building both the general marketplace database and the car-sharing database through the same ongoing process meant the platform could grow both sides of its offering without managing two separate data pipelines. Precise, coordinate level location data meant the app could show genuinely nearby listings, which matters more for a neighborhood-focused marketplace than almost any other kind of platform.
Mobile App Scraping That Respects Both the Source and the Neighborhood
A neighborhood marketplace lives or dies on whether what it shows users is actually nearby, and that only works if location data is precise down to coordinates rather than a rough area. Mobile app scraping for a platform like this also has to respect the sites it pulls from, a fast, aggressive crawl risks breaking the very sources the marketplace depends on every month.
That balance, slow and deliberate crawling paired with coordinate level precision, is what turned 10 to 20 outside sites into a steady, dependable stream feeding two different databases the marketplace could actually build a mobile-first business on.
