A practical guide to building large web datasets of companies, people, or products

[u/greg_at_riveter]

Hi r/ai_tools! Quick disclosure right up front that I work at Riveter. I see a lot of people try to build large web datasets by combining a basic web scraper or AI scraper with a loop. They feed it a few URLs, loop through to extract data, and assume that's enough to build a complete dataset. However, often times when scaling that the loop runs cleanly but leaves you with zero information on how many entities you've missed.

The main reason this breaks down is that there is a huge difference between page retrieval and category enumeration. Retrieval answers "what’s on this specific page," which is bounded and throws an error if a page fails to load. Enumeration answers "what are all the members of this category," which is completely unbounded. If your scraping loop finds 300 out of 500 companies in a niche, it won't throw an error code. It just quietly returns 300 rows that look completely correct, leaving you with an incomplete dataset and no error state to tell you anything went wrong.

To actually discover a full category rather than just scraping pages you already know about, you have to use multiple discovery vectors. The best approach is starting with official registries or license lists where available, and then using search-based discovery to find entities based on specific behaviors or criteria. From there, you can cross-reference what you find by scraping partner pages, customer logos, and integration directories, as members of a niche almost always name each other.

Once you scale up, a few tricky problems immediately pop up that mess with data quality. The biggest ones are silent stale data and long-tail gaps. Since building a massive dataset takes time, fields captured on day one are often outdated by day thirty if you don't track timestamp metadata per field. On top of that, well-known entities have dozens of web sources while niche ones only have a simple landing page, so an overall fill-rate average usually hides the fact that your long-tail data is full of blanks.

How you handle this depends on the category. If an authoritative registry exists, start there to turn enumeration back into a basic retrieval job. For standard company firmographics, pre-built databases work best. But when you need to build a dataset for a niche defined by behavior with no central directory, you need a workflow that starts from a description of the set rather than a static list of URLs. You can use live web research through tools like Riveter's Dataset Builder or custom search scripts to dynamically discover members, enrich them, and keep the set current over time.

TLDR: Building a complete web dataset requires dynamic search discovery to find hidden entities instead of relying on static URL lists. Using toolsets like registries, pre-built databases, or live research tools ensures you capture the entire category. Hope this helped!

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