The Scenario
It's 8:45 on a Tuesday and your sales team's daily standup is at nine. Someone dropped a Google Sheet in Slack last night: 300 inbound leads from yesterday's web form campaign, phone numbers in column B, names in column A, nothing else. The ask: get these into the dialler queue before the team starts calling.
You open the sheet. 300 rows of raw numbers. No spam scores. No flags. No way of knowing which of these are legitimate callbacks and which are known robocall numbers that submitted the form to abuse the system.
The bad version:
- Copy the first number, open the CallerAPI playground in a new tab, paste it, read the spam score, transcribe it into column C by hand.
- Repeat for the second number. Notice you're on number 4 at 8:52 and you have 296 to go.
- Send a Slack message saying the list "needs some cleanup" before it goes to the dialler, which pushes the whole morning's calls back an hour.
Three hundred callbacks postponed because the enrichment step doesn't have a fast path.
Your job is to get the team calling qualified leads. It's not to act as a human API wrapper for 300 numbers before breakfast.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the data in your tabs and talks directly to CallerAPI on your behalf — no API playground, no manual transcription, no per-row copy-paste.
For each phone number in column B of the Leads sheet, look up its reputation using CallerAPI and write the business name (if any), spam score, and FTC complaint count to columns C, D, and E
What You Get
- Column C: the registered business name for the number, or blank if none is on record.
- Column D: the CallerAPI spam score, 0–100. Numbers with no record come back as "Unknown."
- Column E: the FTC complaint count — zero means clean, anything above that means someone has reported the line.
- Any number that returns an API error gets a note in column C so you know it wasn't silently skipped.
What If the Data Is Not Quite Ready
The numbers are formatted inconsistently — some have dashes, some have parentheses, some are missing the country code
Before running CallerAPI lookups, normalize all phone numbers in column B to E.164 format (e.g. +12025551234), then run the spam score lookup and write business name, spam score, and FTC complaint count to columns C, D, and E
Some rows have blank phone numbers mixed in with valid ones
For each row in the Leads sheet where column B is not empty, look up the phone number in CallerAPI and write the business name, spam score, and FTC complaint count to columns C, D, and E. Leave blank rows as-is.
The lead list is split across two tabs — yesterday's batch in Leads and the day before's in Leads Archive — and I need to enrich both
Look up every phone number in column B of both the Leads tab and the Leads Archive tab using CallerAPI. Write the business name, spam score, and FTC complaint count to columns C, D, and E in each tab respectively.
I need to enrich the numbers, flag the risky ones, and move them to a separate tab in one go
For each phone number in column B of the Leads sheet, look up its reputation in CallerAPI and write the business name, spam score, and FTC complaint count to columns C, D, and E. Then move any row where the spam score is above 50 or FTC complaints are greater than 0 to a new sheet called Flagged Numbers, leaving the clean rows in place.
Running the enrichment and the triage in a single prompt means you hand the dialler a clean list — not a raw one you still have to filter.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with inbound lead phone numbers, then ask it to enrich each row with CallerAPI's spam score and FTC complaint count. See also: classifying numbers by carrier and line type and the CallerAPI overview.