The Scenario
You inherited the CRM admin role three weeks ago, and the first thing you found was a 1,000-row company export where "Acme Corp," "ACME Corporation," and "Acme Co." are treated as three separate accounts. Before you can re-import anything clean, you need them grouped.
The bad version:
- Open the export in a sheet, sort by company name, and scan rows manually for variants — a process that works for the first 50 rows and breaks down entirely by row 400.
- Try a fuzzy VLOOKUP formula and spend 90 minutes learning it only handles exact prefixes.
- Export to Python, install a library, write a loop, paste results back, and discover the row count drifted somewhere in the middle.
The company you just joined has 4,000 more rows like this in a different table. There's no budget for a data engineering engagement to clean a CRM export.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that reads your Google Sheet and calls Interzoid's company match key API for you — no script, no API documentation required.
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 sheet 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, distinct keys for genuinely different organizations.
- The sheet re-sorted so "Acme Corp," "ACME Corporation," and "Acme Co." land in adjacent rows.
- Rows with ambiguous names (single-word entries, numbers-only) noted in a status column.
- The canonical grouping is then ready for a second prompt asking SheetXAI 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 in sequence.
Try It
Get the 7-day free trial of SheetXAI and open the Google Sheet with your CRM export — ask SheetXAI to cluster the company names in column A before you touch the re-import. Then check out the spoke on normalizing vendor names to canonical forms or the full Interzoid integration overview.