Case Studies

What we find when we run the data ourselves.

Five cases from independent scraping and QA work. In most, the client was already paying a vendor for this data. None of these gaps were visible until someone ran it independently.

Coverage Healthcare provider directory company
+6.5%

The vendor was delivering less data than the source contained.

On a nationwide provider directory, we returned 79K more providers than the previous vendor on the same URL list. The client had no way to know. Across similar state-based directories, we consistently return over 5% more in-state providers.

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Accuracy & completeness Alternative data provider
30

Records were being delivered. They just weren't complete or accurate.

Across 30 sources, the previous vendor delivered records without the entity identifier needed to match them to other datasets. The vendor said it couldn't be pulled. It was there the whole time, hidden in the page behind the visible view.

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Revenue unlock Travel & hospitality data company
3

The sites weren't impossible. They were uneconomical.

Three high-value travel sources were on the company's target list, quoted elsewhere at over $10,000 per run. We re-engineered the approach and the bypass and brought it to $1,500, making all three viable to package and sell.

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Cost efficiency Real estate data company
75%

The vendor's price looked 40% cheaper. It wasn't.

The vendor's volume discount looked 40% cheaper per row. But they billed for 6.7× more rows than the data held, making their total roughly 4× ours. We deduplicate and scope to what's relevant: about 75% lower for the same data.

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Quality assurance E-commerce reseller
60%

Wrong color, wrong size, wrong product. Caught before the buyer.

An e-commerce reseller had tens of thousands of live listings a month, many on Amazon pages they didn't control. We scraped every listing, flagged image and size mismatches with AI, and sent the edge cases to human review. Listing errors fell from 5% to 2%.

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