The Scenario
You're a CRM admin three weeks into a new role. The first thing on your list: the 1,000-row company export you inherited has "Acme Corp," "ACME Corporation," and "Acme Co." as three separate accounts. The re-import into the new CRM is blocked until the duplicates are resolved.
The bad version:
- Sort the Excel workbook alphabetically and scan adjacent rows — which works until row 400, when the formatting variants stop sorting consistently.
- Build a helper column using SOUNDEX — which generates a key that's too approximate and clusters companies that share a first word but are entirely different organizations.
- Write a Python script, run it on a CSV export, paste results back, discover the row count drifted somewhere in the loop.
There are 4,000 more rows like this in a different table. A data engineering engagement to clean it isn't in the budget.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that reads your Excel workbook and calls Interzoid's company match key API — no script, no API documentation, no export loop.
For each company name in column A (1,000 rows), call Interzoid to generate a company match key and write it into column B, then sort the workbook by column B so duplicates cluster together.
What You Get
- Column B filled with Interzoid similarity keys — identical keys for variants of the same company.
- The workbook re-sorted so "Acme Corp," "ACME Corporation," and "Acme Co." are adjacent.
- Rows with ambiguous names noted in a status column for manual review.
- The canonical grouping ready for a follow-up prompt to pick the most common variant per cluster.
What If the Data Is Not Quite Ready
Column A has mixed casing and extra whitespace
For each company name in column A, trim whitespace and normalize to title case first, then call Interzoid to generate the match key and write it to column B.
Some rows have legal suffixes like "LLC" or "Ltd" that skew the match
For each company name in column A, strip common legal suffixes (LLC, Ltd, Inc, Corp, GmbH) from a copy in column B, then call Interzoid to generate match keys for the stripped names and write them to column C.
You need the canonical name written out, not just the key
Group rows by match key in column B and write the most common name variant for each cluster into column C as the canonical company name.
Full cleanup in one shot
For each company name in column A: trim and normalize casing, call Interzoid for the match key, write the key to column B, then group by key and write the canonical (most-frequent) variant to column C. Flag any cluster with only one member as 'UNIQUE' in column D.
One prompt handles the normalization, the API call, the grouping, and the status tagging.
Try It
Get the 7-day free trial of SheetXAI and open the Excel workbook with your CRM export — ask SheetXAI to cluster the company names in column A before you push to the new system. Then check out the spoke on normalizing vendor names to canonical forms or the full Interzoid integration overview.