The Scenario
You joined the data team three weeks ago. One of your first projects is cleaning up the accounts database before it migrates to the new CRM. The handoff doc says "the company names are a mess" and it is not wrong. You are looking at a Google Sheet with 150 company names entered freeform by reps over two years: "Acme Inc", "ACME", "acme corp", "Acme, Inc.", all referencing the same company. Before you can deduplicate, you need a canonical name for each. PDL has a company cleaner endpoint that resolves variations to a standard record.
The bad version:
- Look up each variation in the PDL API console one at a time and copy the canonical name, website, and LinkedIn back into the sheet by hand
- Spend 45 minutes on the first 30 rows before realizing you have no way to tell if "Acme Technologies" and "Acme Corp" should resolve to the same canonical record without clicking through each one
- Deliver a half-cleaned sheet to the CRM migration lead, who kicks it back asking you to finish before Monday
The CRM migration is not waiting on the company names. The company names are waiting on you to get through 150 rows of cleanup that a process should handle.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the raw company name column and uses PDL's company cleaner to standardize each entry and write the canonical name, website, and LinkedIn URL back into the sheet.
For each company name in column A, clean it via PDL and write the standardized name, website, and LinkedIn profile back into columns B, C, and D.
What You Get
- Column B populated with the canonical company name from PDL's database
- Column C populated with the company's primary website
- Column D populated with the LinkedIn company page URL
- Rows where PDL cannot resolve the name flagged in column E so you can review them before the migration
What If the Data Is Not Quite Ready
Some rows are clearly the same company but have trailing punctuation or legal suffixes
Before cleaning, strip legal suffixes like Inc, LLC, Corp, and Ltd from each name in column A. Then run PDL company cleaner on the stripped names and write canonical name, website, and LinkedIn to columns B, C, and D.
You want to keep the original and the cleaned name side by side for review
For each company name in column A, clean it via PDL and write the canonical name to column B, the website to column C, and the LinkedIn profile to column D. Keep the original name in column A unchanged so the migration team can spot-check the mapping.
Some rows are subsidiaries and should resolve to the parent company
Clean each company in column A via PDL. If PDL returns a parent company that differs from the match, write both the matched company name in column B and the parent company name in column E so the team can decide which to use for the CRM hierarchy.
Clean names, deduplicate by canonical form, and flag surviving rows in one prompt
For each company in column A, clean via PDL and write the canonical name to column B. Then group rows by canonical name and flag duplicate rows in column C as "Duplicate of row X" so the migration team can delete them without losing the primary record.
One prompt handles the cleanup and the deduplication pass — the migration lead gets a sheet that is ready to import, not one that still needs another round of review.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of messy company names from reps or form submissions. Ask it to clean each one via PDL and write canonical names back. Then see how to enrich cleaned company names with firmographic data or go back to the People Data Labs overview.