The Scenario
You're a sales ops analyst at a B2B services firm and your CRM just exported 80 prospect records into an Excel workbook. Company name in column A, domain in column B — that's it. Your job before the next pipeline review is to add founding year, estimated employee count, and most recent funding round for each company. The sales team uses these signals to prioritize outreach.
The data enrichment vendor your company normally uses doesn't cover the long tail of your prospect list. About 30 of the 80 companies are too small or too recent to appear in the major databases. Someone will have to look them up manually.
The bad version:
- Search the web for "Acme Corp founding year", find a Crunchbase entry, note the year, go back to the workbook, enter it in column D
- Search for headcount: check the LinkedIn company page, note the range displayed, enter it in column E
- Search for most recent funding: check TechCrunch, Crunchbase, and the company's own press releases, enter it in column F
- Do this for 80 companies, averaging 6-8 minutes per company
That's somewhere between 8 and 11 hours of lookup work. You were hired to analyze pipeline, not to manually populate worksheet columns with public company facts.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands your data, and through its built-in Exa integration it can search the web for each company and write founding year, headcount, and funding data back to the right columns — without you touching a single cell manually.
For each company in my Excel workbook with the company name in column A and domain in column B, search Exa for founding year, estimated headcount, and latest funding round, then write those values into columns D, E, and F
What You Get
- Column D fills with the founding year sourced from Exa results
- Column E receives the estimated headcount or headcount range
- Column F receives the most recent funding round (amount, series, and date where available)
- Column G notes which rows had low-confidence results or no data found, so you can spot-check those manually
What If the Data Is Not Quite Ready
Company names in column A have formatting inconsistencies
Some entries say "Acme, Inc.", others say "ACME International", others are trading names rather than legal names.
Before searching, normalize the company names in column A — strip suffixes like ", Inc.", ", LLC", ", Ltd" — then use Exa to search for founding year, headcount, and latest funding for each cleaned name and write results to columns D, E, and F
You only want to enrich rows above a revenue threshold
Your CRM tagged estimated annual revenue in column C. You only want to enrich companies marked as "$10M+".
For each row where column C contains "$10M+", search Exa for the company name in column A to find founding year, headcount, and latest funding round, and write the results to columns D, E, and F — skip rows where column C is blank or below threshold
Some rows already have partial data
A first pass of manual enrichment filled column D (founding year) for some rows but left columns E and F empty.
For each row where column E or column F is blank, search Exa for the company in column A and fill in the missing headcount (column E) and latest funding round (column F) — leave column D untouched for rows that already have it
Full enrichment pipeline: normalize, filter by priority, fill gaps, flag low confidence
Normalize company names in column A by stripping legal suffixes, then for each row where column C is "$10M+" or above, search Exa for founding year, headcount, and latest funding round — write results to columns D, E, and F, and write "Low confidence" in column G for any field where Exa returned uncertain or ambiguous data
One pass handles the cleanup and the enrichment together.
Try It
Get the 7-day free trial of SheetXAI and open any prospect workbook with company names and domains, then ask it to use Exa to fill in the firmographic fields for each row. For related reads, see how to run deep research briefs for a list of topics or run bulk company research with citations.