Data Extraction Tool for Pages, PDFs Behind Logins, and Lists

A data extraction tool converts unstructured sources, web pages, listings, tables, documents, into structured records. WebRobot does this with an AI agent: you name the fields you need, it finds them across pages, normalizes the values and delivers clean rows.

Extraction is broader than scraping one list. Real datasets are assembled from index pages, detail pages, embedded tables and downloadable files, often behind a login. One robot handles the whole chain.

Run the robot

01 / SOURCES FIG. 1 · EXTRACTION SURFACES

Four surfaces, one robot

Most tools stop at "the visible list". WebRobot chains surfaces together in a single run, the way an analyst would work through them by hand.

SURFACE 1

Lists and tables

Product grids, directories, search results, HTML tables. The robot follows pagination and "load more" buttons until the set is complete, then deduplicates.

SURFACE 2

Detail pages

The robot clicks into each result and pulls fields the list view hides: full specs, contact emails, descriptions, image URLs. One row per entity, merged automatically.

SURFACE 3

Files and PDFs

Linked price lists, spec sheets and PDF catalogs are downloaded and parsed into the same columns as the page data. No separate document pipeline.

SURFACE 4

Behind logins

Supplier portals, member directories and dashboards you have credentials for. Agent actions (Scale plan and up) sign in and navigate like a person.

02 / FIELD MAPPING FIG. 2 · RAW TO STRUCTURED

How messy source data becomes typed columns

You describe fields once; the robot maps and normalizes them on every run. A real mapping from an industrial supplier catalog:

On the page	Mapped field	Normalized output
"DIN 933 Hex Bolt M8×40, zinc"	product_name	DIN 933 Hex Bolt M8x40 Zinc
"€ 12,90 / 100 pcs (excl. VAT)"	unit_price_eur	0.129
"Usually ships in 2-3 days"	availability	in_stock
Spec sheet PDF, page 2	tensile_class	8.8
Detail page breadcrumb	category	Fasteners / Bolts

Because the mapping is semantic, not positional, it survives redesigns: when the supplier moved prices into a tab last quarter, robots re-mapped in an average of 41 seconds. That self-healing behavior comes from the underlying AI web scraper engine.

03 / DELIVERY FIG. 3 · OUTPUT MANIFOLD

Extracted once, delivered everywhere

For spreadsheets and ops

CSV and Excel downloads, or a Google Sheet that refreshes on schedule. The fastest path is the scrape website to Excel workflow, and setup stays sentence-simple with the no code web scraper approach.

For pipelines and monitoring

Clean JSON from the web scraping api, webhooks into your warehouse, or field-level alerts via website change monitoring when you care about deltas rather than dumps.

04 / FAQ FIG. 4 · ANALYST QUESTIONS

Data extraction tool questions

Web scraping usually means pulling visible page content. Data extraction is the wider job: combining list pages, detail pages, files and logged-in areas into one structured, normalized dataset. WebRobot does both with the same robot, so the distinction never costs you a second tool.

It normalizes as it extracts: currencies and units parsed to numbers, dates to ISO format, whitespace and encoding artifacts stripped, duplicates removed across pages and runs. You define the shape once and every run conforms to it.

Launch ($79 per month) includes 10k records, Scale ($249) includes 100k with logins and multi-step flows, Autopilot ($699) includes 1M with real-time monitors. Across the fleet, WebRobot extracted 38M rows last month.