The Scenario
Your data team has a list of 300 product listing URLs in an Excel workbook. Someone needs to scrape them all — title, price, availability — to feed the weekly inventory dashboard. The previous approach was a Python script that ran sequentially and took two hours to finish, regularly dying at row 180 when a block of URLs hit rate limits in sequence.
The migration to async batch scraping was supposed to be somebody's project last sprint. It is now this sprint, and the dashboard refresh is tomorrow.
The bad version:
- Submit URLs to Scrape.do's async endpoint in batches manually, keeping track of which job IDs correspond to which row ranges
- Poll each job ID's status endpoint every few minutes until it completes
- When results arrive, match them back to the correct rows by job ID and write them in
For 300 URLs spread across multiple jobs with different completion times, this is not a workflow — it is a second full-time task running alongside the actual work.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the URL list, submits all 300 as a single Scrape.do async batch job, monitors completion, and writes the results back into column B when the job finishes — no polling, no manual job ID tracking.
Submit all 300 URLs in column A of my Excel workbook as a single Scrape.do async batch job and write the scraped HTML result for each URL back into column B when done.
What You Get
- All URLs in column A are submitted as a single async job to Scrape.do
- SheetXAI waits for job completion without you polling manually
- Column B fills with the scraped HTML response for each URL in order
- Rows where a specific URL fails within the batch show an error label rather than silently missing
What If the Data Is Not Quite Ready
URLs need cleanup before submission
Clean column A first: trim whitespace from each URL, remove any blank rows, and deduplicate exact matches. Then submit all cleaned URLs as a single Scrape.do async batch job and write the scraped HTML into column B when the job completes.
You want plain text, not HTML
Submit all URLs in column A as a Scrape.do async batch job. When the job completes, extract the plain-text page content from each response — no HTML tags — and write it into column B.
Some URLs need JavaScript rendering
For URLs in column A where column C says "render", submit as a Scrape.do async batch job with render=true. For all other URLs, submit without rendering. When both jobs complete, write the scraped content into column B for all rows.
Full pipeline — cleanup, batch, and structured extraction in one shot
Clean column A: trim whitespace and remove blank rows. Submit all URLs as a Scrape.do async batch job with render=true. When the job completes, extract the product title and current price from each page and write them into columns B and C. Flag any URLs where the response was not 200 by writing the status code into column D.
Cleanup, batch submission, completion handling, and structured field extraction — all in one ask.
Try It
Get the 7-day free trial of SheetXAI and open the Excel workbook with your large URL list, then ask it to submit the batch job and populate the results. See also the spoke on standard URL scraping for smaller lists, or the hub overview for all Scrape.do workflows.