Solution
For pricing, revenue management, and travel intelligence teams: fare data from hundreds of differently built airline sites, collected at production scale without a per-site scripting team.
The problem
Each airline site has its own structure, navigation, and booking flow. The script-per-site approach needs a standing developer team just to hold coverage steady.
Destination sites aggressively block automated traffic. Conventional crawlers get denied exactly when the fare data matters most.
Adding one airline means a scripting sprint; a site redesign means a rewrite. The engineering queue decides how fast the intelligence product can grow.
The product, not a promise
How it works
A site-specific crawler is created on the no-code platform — point and click, no script.
The AI-embedded intelligent browser moves through the site the way a human traveler would.
Fare and availability data is read from the pages, whatever each site's structure.
Comparable pricing data flows into the client's real-time intelligence products.
Who it's for
Pricing analyst
Intelligence product owner
Head of infrastructure
A leader in hospitality and travel technology powers the industry with real-time intelligence — and real-time fare intelligence means mining hundreds of airline websites, continuously, at the scale of a million searches per month. The traditional approach breaks in three places at once: every site has a different structure, so each needs its own hand-built script; maintaining those scripts needs a standing team of developers; and destination sites aggressively block automated crawlers. The client’s growth depended on scaling all three past their ceilings.
The Botminds platform makes crawler creation a no-code activity. A site-specific crawler is configured through point-and-click, which means covering a new airline site is a task for an analyst, and a site redesign is a reconfiguration an analyst completes the same day. That single change converts crawler maintenance from a headcount problem into an operations task — and it is what made rapid onboarding of new sites possible.
The blocking problem needed a different answer. The platform’s AI-embedded intelligent browser navigates destination sites by mimicking human browsing behavior, moving through search flows the way a traveler would. Sites that shut out conventional automation kept serving pages.
A million searches per month is infrastructure. The solution runs on-premises in the client’s environment, giving them direct control over the capacity behind their intelligence products and keeping the crawling inside their own infrastructure.
The combination — no-code coverage of hundreds of differently structured sites, human-like navigation that survives blocking, on-prem scale — turned fare comparison into a production data pipeline the business could grow on. The same architecture applies wherever competitive data lives on many websites that were never designed to be read by machines.
Objections, answered
Each data point carries the site, search parameters, and page it came from, so any fare in the feed can be checked against its source. Unrecognized fare structures are flagged for review instead of guessed at.
The crawler reads pages by meaning rather than fixed selectors, so most layout changes pass through without intervention. When a redesign does need attention, an analyst reconfigures the crawler through the same point-and-click interface — no parser rewrite, no developer queue.
The AI-embedded intelligent browser navigates search flows the way a traveler would, at controlled rates, rather than hammering endpoints. Sites that shut out conventional crawlers kept serving pages throughout the engagement.
Yes — the reference deployment runs on-premises in the client's own infrastructure and sustains millions of searches per month. You control the capacity directly, and the crawling stays inside your environment end to end.
Watch a no-code crawler get configured against it live, navigate the booking flow, and return structured fare data.
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