Scraping Pros

Delivery platforms capture 15–23 data points per transaction. Fewer than 3% of those reach restaurants, operators, or investors in actionable form. In a $600B ecosystem, that gap is where competitive advantage is built.

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Not every team that needs web data has an engineer to build a scraper. A marketing manager tracking competitor pricing, a real estate analyst pulling listings, or an ecommerce seller monitoring product reviews all need the same thing: structured data from websites, without writing a line of Python. That’s the gap no-code

Most companies discover the limits of DIY web scraping the same way: a script that worked perfectly in staging breaks in production, a cloud vendor blocks their IP range overnight, or a site redesign wipes out six months of selector logic. By the time the data team patches it, three weeks of

Most data teams build their scraping infrastructure the same way they think about their markets: one industry at a time. A retail team has its pipeline, the logistics team has another, and the finance unit maintains a third. Each works. Each delivers something. But here’s what gets lost in that arrangement: the signals that only become visible when you look across all three a...

In retail, price is the most immediate argument a consumer has for choosing or abandoning a purchase. But behind that visible number lies a real-time decision infrastructure: repricing algorithms, margin rules, seasonal strategies, and competitive responses that update multiple times a day. The problem for most retailers is not a lack of data — it is the speed at which that d...