Custom Parsing

We can collect any public data that's visible on a website through a normal browser — prices, products, reviews, contact details, classified listings, and similar structured or semi-structured content. The general rule is: if a human visiting the site in a browser can see the information, we can usually build a parser to extract it programmatically. This covers cases that don't fit our standard service pages, such as a niche industry directory, a regional classifieds site, or a competitor's site with an unusual page layout that a generic scraping tool can't handle.

Cost depends on three main factors: how complex the source website is to parse (simple static pages cost less than sites with heavy bot protection or dynamic content loading), how much data volume you need collected, and what output format and delivery schedule you require. Because every custom project is different, we don't have a fixed price list for this service — instead, we offer a free consultation where you describe what you need, and we provide a specific quote based on your actual requirements rather than a generic estimate.

To get started, send us a link to the source you want data collected from, along with a description of exactly what data you need from it. We typically prepare an initial proposal within 24 hours, outlining the approach, estimated timeline, and cost. If your requirements are unusual or the source has complications — login walls, CAPTCHAs, or heavy bot protection — we'll flag that during this initial review so there are no surprises once the project is underway.

Data is delivered as CSV, XLSX, or JSON, or through a direct API if you need to pull it into your own systems programmatically. We can also integrate with Power BI, your ERP, or other internal infrastructure if that's a better fit than working with raw export files. Since every custom project starts from a specific need, we confirm the delivery format with you during the initial scoping conversation, rather than defaulting to one option that might not match how your team actually uses the data.