Solution
For market intelligence, data operations and credit teams: targeted web sources turned into a live, schema-consistent dataset — without a maintenance backlog of hand-written scrapers.
The problem
Rule-based crawlers fail on every site redesign, and the maintenance backlog grows until the data quietly stops arriving.
Analysts copy values from portals into spreadsheets — slow, error-prone, and impossible to audit afterward.
The rate changed, the regulation was amended, the product was delisted — and the dataset nobody re-crawled still says otherwise.
The product, not a promise
How it works
Define target sites, portals, and databases with point-and-click crawler setup — no scraping code to write.
Record-and-replay navigation reaches the right pages, static or dynamic, with built-in throttling.
Schema-aware extraction reads the fields you defined and normalizes them into one consistent structure.
Change detection re-visits pages and raises delta alerts when values move.
Structured datasets flow to downstream systems through APIs and data pipelines, or as direct downloads.
Who it's for
Intelligence analyst
Head of data
IT & compliance
Every web data project has two separate problems: reaching the page and reading the page. Most teams solve the first with rule-based crawlers that break every time a site changes, and the second with people copying values into spreadsheets. Web Intelligence Harvester replaces both.
You define the target — competitor sites, regulatory portals, public registries, supplier price lists, online databases — with point-and-click setup rather than scraping code. Record-and-replay navigation lets crawlers reach content behind menus, filters, and dynamic rendering, and built-in throttling keeps crawling well-behaved. Extraction is schema-aware: you declare the fields once, and the system reads them consistently across pages and sites, normalizing everything into a single structure.
The harvester then keeps watching. Change detection re-visits monitored pages and raises delta alerts when a value moves — a rate updated, a regulation amended, a product delisted. Instead of a one-off scrape, you get a live, versioned dataset. Teams use it to track competitors, regulatory updates, and market signals, and lenders use it to enrich credit files with registry and public-source data.
Web data is only useful if you can say where it came from. Every extracted value carries its source URL and capture time, so anyone downstream can open the exact page a number was read from. Crawler quality is checked and improved through feedback from your subject-matter experts — when extraction drifts, a reviewer corrects it once and the correction sticks.
Output goes wherever the work happens: download formats for analysts, or out-of-the-box integrations and APIs that feed downstream systems directly. The result is document-to-decision intelligence from the open web — structured, current, and traceable — without a maintenance backlog of hand-written scrapers.
Objections, answered
Every extracted value carries its source URL and capture time, so anyone downstream can open the exact page a number was read from. When a site layout changes, the affected extraction is flagged for review instead of silently returning wrong values.
You declare the schema once — the fields you need — and map each source with point-and-click, record-and-replay setup. The same schema then applies across every site, so adding a source is configuration, not a new scraper project.
Built-in throttling and controlled navigation keep collection polite on every source, and the capture record documents exactly what was accessed and when. The platform deploys in your cloud environment, and collected data stays under your access controls.
Days. Mapping a source is a point-and-click session, extraction starts on the first crawl, and monitoring begins immediately after. Teams typically start with a handful of high-value sources and extend from there.
Watch a crawler get built point-and-click and return schema-clean, source-linked data from it in a live demo.
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