A social media intelligence house needed a media monitoring API that could aggregate news and social feeds from more than 5,000 geographically scattered sources, searchable across 1,000+ keywords, without the geo-tagging errors its own in-house attempt kept producing.
Client Overview
A social media intelligence firm needed to aggregate news and social media feeds from more than 5,000 sources scattered across different geographic locations, then make all of it searchable across more than 1,000 keywords and specific queries. The firm had already tried building this in-house, and the result fell short on both fronts that mattered, the data was thin in quantity and inconsistent in quality.
The most damaging problem was geography itself. The firm reported that the location tagged to a feed was wrong in numerous cases, which defeats the entire purpose of a media monitoring API meant to serve geographically specific intelligence. Getting location right, not just collecting more data, is what actually brought the firm to PromptCloud.
Client Requirements
The firm’s brief to PromptCloud went beyond scale alone:
- News and social media feeds aggregated from more than 5,000 sources across various geographies
- Accurate geographic tagging for every feed, correcting a problem the firm’s own in-house attempt could not solve
- Searchability across more than 1,000 keywords and specific queries
- Delivery in a structured, collated format the firm could import weekly
- A source, location, and keyword list that could change as the firm’s own requirements evolved
Challenges
Aggregating feeds from more than 5,000 scattered sources is a scale problem on its own, but scale was not actually the firm’s biggest issue. Its in-house attempt had already tried to collect broadly and still came up short on both quality and quantity, which pointed to a structural problem rather than simply needing more crawling power.
Geographic accuracy was the real failure point. A media monitoring API is only useful to an intelligence firm if the location attached to each feed is actually correct, and the firm’s existing setup was miss-tagging locations often enough to undermine trust in the data altogether. Solving this meant building real geographic verification into the pipeline itself, not just collecting feeds and hoping the location metadata attached to them was accurate.
Solutions
PromptCloud built this around a mass scale crawl paired with a custom geographic verification layer, treating location accuracy as a first class requirement rather than an afterthought.
Mass Scale Crawling Without Overloading Sources
Crawling more than 5,000 sources in parallel, at regular intervals throughout the day, needed real infrastructure discipline, still respecting the politeness policies that keep a crawler from hitting any single source too aggressively. This kind of mass scale crawling is a meaningfully different engineering problem from a handful of scheduled scrapes, worth understanding when comparing PromptCloud’s approach to other web scraping tools built for smaller, less distributed jobs.
A Geo-Intelligence API Built to Fix the Location Problem
Rather than trusting whatever location metadata a source happened to expose, PromptCloud built a dedicated Geo-Intelligence API to verify and assign location as feeds were collected, so the output could actually be trusted for geographically specific intelligence. This directly addressed the firm’s core complaint, incorrect location tagging, rather than simply collecting more data on top of an unreliable location layer. Feeds were captured only from the locations they were actually meant to represent, not wherever the source happened to claim.
A Source List That Moved With the Firm’s Needs
Sources, locations, keywords, and queries were never treated as fixed. As the firm’s own requirements and feedback evolved, the list was modified dynamically rather than requiring a new project each time priorities shifted. That flexibility mattered as much as the initial scale, since a static list would have started falling behind the firm’s actual intelligence needs within weeks of going live.
Weekly Delivery, Collated by Location
Fresh data was collated location-wise and delivered every week, giving the firm a structured import it could load directly rather than a raw dump needing further sorting. Over 200,000 feeds were collected across multiple continents within the first 2 months alone, a volume and geographic spread that would have been difficult to reach with the firm’s earlier in-house approach.
Social Media Feed Aggregation, Before and After PromptCloud
| Area | Before | After |
| Geographic accuracy | Location often mistagged, undermining trust | Geo-Intelligence API verifies location at collection |
| Scale | In-house attempt fell short on quality and quantity | 5,000+ sources, 200,000+ feeds in 2 months |
| Searchability | Not addressed by the in-house attempt | Searchable across 1,000+ keywords and queries |
| Source list | Static, hard to adjust | Dynamically modified based on feedback |
Benefits to the Client
The firm finally got feeds it could trust were actually from the locations they claimed, solving the exact problem that undermined its in-house attempt. More than 200,000 feeds arrived within the first 2 months alone, spanning multiple continents, structured and collated by location rather than delivered as an undifferentiated pile of data.
Weekly delivery meant the firm could import fresh data on a predictable schedule rather than processing a constant, unstructured stream, and searchability across more than 1,000 keywords and queries meant its own analysts could actually query the data meaningfully. None of this required a larger internal team, since PromptCloud handled parallel collection, geographic verification, and delivery as one continuous pipeline.
A Media Monitoring API Is Only as Good as Its Location Data
Collecting more feeds was never the real problem here, trusting where those feeds actually came from was. A media monitoring API that gets geography wrong undermines every piece of intelligence built on top of it, no matter how many sources it claims to cover.
That is what changed the outcome, building geographic verification directly into the collection process rather than treating location as metadata to sort out later. That is what turned 5,000-plus scattered sources into a feed the firm could actually trust by location, not just by volume.
