Solution
For market intelligence, pricing, and product teams: competitive data collected from any site and delivered structured into your systems — configured through point-and-click, reviewed by your analysts.
The problem
Regex and XPath work until the page changes, which pages do constantly. Every competitor redesign lands another broken selector in the developer queue.
A selector can find a string; it cannot read a page. So a data-entry team quietly does the half of the job the automation never covered.
Throttling, bot detection, and blocking mean the crawler often never gets to the page it was built to read.
The product, not a promise
How it works
Build a site-specific AI crawler with a few point-and-click actions — no code.
Built-in throttling and blocking countermeasures get the crawler to the page at scale.
The AI understands the page and extracts the information you defined — no regex, no XPath.
Structured competitive data flows into your downstream systems automatically.
Who it's for
Market intelligence analyst
Pricing lead
Engineering lead
Every competitive monitoring effort has to solve two orthogonal problems: reaching the page and reading the page. Most organizations solve neither well. They deploy rule-based crawlers that need constant manual maintenance to keep reaching pages, and they fall back on manual data entry because the reading problem — turning an arbitrary web page into the specific facts you care about — resists automation built on pattern matching.
The standard toolkit is regex and XPath: locate the price with a selector, extract it with a pattern. It works until the page changes, which pages do constantly. Every competitor site redesign breaks selectors; every broken selector needs a developer; and the automated monitoring program quietly becomes a semi-automated one with a human backlog behind it. Meanwhile the reading problem stays open — a selector finds a string, and understanding a layout it has never seen is beyond it.
The Botminds approach covers both halves. Creating a crawler is a few point-and-click activities — you show the platform a site, define the information you want, then deploy and scale to suit your need. For reaching pages, the platform ships with at-scale crawling mechanisms that handle throttling and the other blocking challenges destination sites throw at automation.
For reading pages, the crawler is backed by AI that understands page content. That is what removes the manual data entry step: extraction survives layout changes because it is anchored to meaning rather than to an XPath.
The end state is one solution covering the whole chain — crawl, understand, extract, integrate downstream. Competitive monitoring becomes something you configure and review rather than something you staff. And because extracted data lands in your systems with its source page attached, the intelligence your team acts on is checkable.
Objections, answered
Every extracted value lands with its source page attached, so your analysts verify against the actual page in one step. Where the platform is uncertain — an ambiguous price display, an unusual layout — the item is flagged for human review rather than guessed.
Yes. You show the platform a site and define exactly the information you want — your fields, your definitions. The crawler extracts to that specification, and you refine it as your monitoring questions change.
Extraction is anchored to what the page means rather than where elements sit in the markup, so it survives most layout changes without intervention. The rare change that does need attention is a reconfiguration, handled through the same point-and-click interface.
A first crawler is a point-and-click session, and most teams have their first sites feeding downstream systems within days. Coverage then grows site by site without an engineering dependency.
Watch a crawler get built against it live — reach the page, read it, and deliver structured data with the source attached.
Request a demo