Yes. 240 exhibitors into rows, without writing a scraper

What does a worked extraction look like?

A sales team wants the exhibitor list from a trade show: 240 exhibitors across 12 directory pages, each with a detail page. The instruction is the schema (company, booth, category, website, city, one-line description) plus the source.

The companion pages through the directory, opens all 240 detail pages, writes the rows into a new sheet with a source link per row, and flags 9 exhibitors whose detail pages were missing fields rather than inventing values. Minutes of machine work; your part is defining the columns and spot-checking ten rows.

What can it extract that a scraper cannot?

The judgment-adjacent fields. A scraper needs a selector; a companion reads the page. That shows on messy sources: data living in prose rather than tables ("family-run since 1987, about 40 employees" becomes headcount: ~40), inconsistent layouts across detail pages, and fields that need light interpretation, like whether a listing is a distributor or a manufacturer.

It also shows on sources behind your logins: member directories, portals, dashboards, anywhere the logged-in session is the price of entry. Prompting patterns for this kind of run are collected in how to prompt AI for web scraping.

Where the sheet goes next

Usually into the next job: deduped against the CRM, then enriched, then used for outreach, which is where this joins list-building work.

Re-running the same extraction on a schedule turns it into monitoring, with only the changed rows reported. Sheet-level specifics live on the Google Sheets integration page.

What to put in the instruction

Name the columns, name the source, and say what to do with a missing value.

"Flag it, do not guess" is the line that turns a plausible sheet into an auditable one.

Add a source URL column and your ten-row check has somewhere to start.

Frequently asked questions