The Scenario
Your pipeline review is Friday morning. You have an Excel workbook with 400 target accounts — company name in column A, domain in column B — and columns C through F are empty. The VP of Sales wants headcount, latest funding round, and 6-month headcount growth attached to every row before the slides go out Thursday evening.
The bad version:
- Open Crustdata's web interface, search each company name one at a time, copy the headcount and funding fields into the right row
- Hit a naming mismatch on row 38 — "Acme Corp" vs. "Acme Corporation" — and spend 15 minutes figuring out which is correct
- Finish 60 rows by lunch, realize you have 340 left, and accept that the data will be incomplete when the deck lands
There is no version of that process that gets 400 rows done by Thursday. The constraint is not effort — it is time.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that runs inside your Excel workbook. It reads your worksheet structure, connects to Crustdata, and can enrich your full list in one pass based on a single typed instruction.
Look up each company in my Excel sheet using Crustdata's company screener and fill in industry, total funding amount, and last funding date for all 400 rows — write results into columns C, D, and E and flag any rows where Crustdata returned no match in column F.
What You Get
- Column C: industry classification from Crustdata's dataset
- Column D: total funding raised in USD
- Column E: date of last funding round in ISO format
- Column F: a "no match" flag for any company name that did not resolve so you can review those rows rather than receiving blank cells without explanation
What If the Data Is Not Quite Ready
The company names in column A have extra legal suffixes
For every company in column A, strip legal suffixes before passing the name to Crustdata, then fetch current headcount and latest funding round — write results into columns C and D and flag unmatched rows in column E
The list has duplicate companies at different rows
Deduplicate column A by company name, then for each unique company use Crustdata to fetch headcount and funding stage — write results back to every matching row and add a note in column F if the row was a duplicate
Headcount growth should be calculated from two specific Crustdata snapshots, not the default window
For each company in column A, use Crustdata to get headcount as of January 2025 and headcount as of January 2026 — write both values into columns C and D, then calculate the percentage change and put it in column E
One prompt to clean the list, enrich it, and score each account
Remove any rows where column A is blank or says "TBD", then use Crustdata to fetch headcount, funding stage, and 6-month growth for the remaining companies — write those into columns C, D, and E, then add a priority score in column F where companies with over 200 employees and Series B or later funding get "High" and everything else gets "Low"
The enrichment and the scoring logic happen in one pass.
Try It
Get the 7-day free trial of SheetXAI and open your prospect pipeline workbook with company names and domains, then ask it to bulk-enrich every row using Crustdata's firmographic data. From there, also see how SheetXAI handles headcount timeseries pulls and job listing signals.