The Scenario
You're a partnership manager at a B2B services firm. A colleague scraped ninety company names from an industry directory into a Google Sheet — column A is full of business names like "Meridian Analytics," "Coastal Freight Solutions," "Northgate Capital Group." No domains, no URLs, nothing you can use to look up contacts, send emails, or run enrichment.
The outreach plan is supposed to launch next week. You've been handed the list and asked to find domains before anything else can happen.
The bad version:
- Google each company name manually, guess which of the three search results is the right website, copy the domain into column B
- Get to "Global Logistics Partners" and find seventeen companies with nearly identical names — spend ten minutes deciding which one matches the directory context
- Finish sixty rows by lunchtime, realize you have thirty left and a 2 PM call where you were supposed to present the outreach plan
Finding domains from names is deceptively slow. The first twenty go quickly. Then you hit the ambiguous ones — generic names, companies that rebranded, names that match holding companies instead of operating entities — and the pace drops off completely.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. Its Bigpicture.io integration includes name-to-domain search, which returns up to three domain candidates per company name so you can resolve ambiguous entries without leaving the sheet.
For every company name in column A, use Bigpicture.io to find up to 3 possible domains and write them to columns B, C, and D. In column E, flag rows where no match was found.
What You Get
- Columns B, C, D: Up to three domain candidates per company name, ordered by confidence
- Column E: A "No Match" flag for rows where Bigpicture.io returned nothing — so you know exactly which names need manual follow-up
- The output is immediately ready for the next enrichment step — take column B (the top match), pass it back through Bigpicture.io for firmographic enrichment, or feed it directly into your outreach tool
What If the Data Is Not Quite Ready
Some company names have legal suffixes that throw off the lookup ("LLC," "Inc.," "Ltd.")
For each company name in column A, strip any legal suffixes (LLC, Inc., Ltd., Corp., GmbH) before running the Bigpicture.io name-to-domain lookup. Write up to three domain candidates to columns B, C, and D. Flag no-match rows in column E.
You only want the top domain match, not three candidates
Look up the most likely domain for each company name in column A using Bigpicture.io. Write the top match to column B and flag rows in column C where no match was found or confidence was below the threshold.
You want to deduplicate company names before running the lookup to avoid wasted API calls
Remove duplicate company names from column A, then run Bigpicture.io name-to-domain search for each unique entry. Write the top three domain matches to columns B, C, and D, and flag no-match rows in column E.
Full kill chain: clean names, resolve domains, enrich the top match with firmographics
For each company name in column A, strip legal suffixes and run a Bigpicture.io name-to-domain lookup. Write the top domain match to column B. Then enrich each domain in column B with Bigpicture.io firmographic data — write industry, employee count, and country to columns C, D, and E. Flag rows in column F where no domain or no enrichment data was found.
The cleaning, the lookup, and the enrichment happen in one pass — no intermediate step of exporting a domain list and re-importing it.
Try It
Open a Google Sheet with a column of company names and get the 7-day free trial of SheetXAI. Ask it to resolve each name to a domain using Bigpicture.io — with optional firmographic enrichment added in the same prompt. For enrichment once you have domains, see the bulk domain enrichment spoke, or return to the Bigpicture.io hub overview.