Copy-paste formulas for a Coresignal alternative workflow in Google Sheets
Paste a formula into row 2, test it on a few rows, then drag down to run the workflow across your spreadsheet.
Firmographic summary
A: company - B: source notes - C: offer
=GPT("Summarize firmographics for this company: " & A2 & ". Source notes: " & B2 & ". Offer: " & C2 & ". Return likely industry, size band, location, and one relevance signal. Mark anything not stated as Unknown.")
Fit score 1-5
A: account - B: ICP - C: source text
=GPT("Score this account 1-5 for fit. Account: " & A2 & ". ICP: " & B2 & ". Source text: " & C2 & ". Return score, reason, confidence, and what to verify manually.")
Personalized opener
A: contact/role - B: signal - C: offer - D: tone
=GPT("Write a specific outreach opener for " & A2 & " based on this signal: " & B2 & ". Offer: " & C2 & ". Tone: " & D2 & ". Keep it factual and under 70 words.")
QA missing-data flag
A: AI output - B: source text - C: required fields
=GPT("QA this output: " & A2 & ". Source text: " & B2 & ". Required fields: " & C2 & ". Return missing data, risky assumptions, unsupported claims, and pass/review/fail.")
Short answer
A Coresignal alternative in Google Sheets means doing research, enrichment, scoring, and personalization with AI formulas in the spreadsheet instead of adopting a separate tool. Coresignal supplies large firmographic and employee datasets pulled from public sources. GPT for Sheets is a lighter, spreadsheet-native option for B2B sales, RevOps, and account research teams who want the research and prioritization layer where their lists already live.
Fastest path: Install GPT for Sheets -> add your source columns -> paste a formula from the formula section -> review 10 rows -> fill down the sheet.
GPT for Sheets is not affiliated with Coresignal and is not a contact database. Coresignal and other product names are trademarks of their respective owners, and the comparison here is factual and non-defamatory.
Workflow
A practical sheet for this workflow usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Company or contact name | Stable row anchor for each record |
| B | Source notes: website copy, export, CRM fields | Keeps AI grounded in inspectable evidence |
| C | Offer or product | Sharpens relevance and scoring |
| D | Target signals to find | Defines what the AI should look for |
| E | AI research summary | First useful interpretation of the row |
| F | Fit score and label | Sorts the list for routing |
| G | Outreach opener or next action | Turns research into execution |
| H | QA flag | Stops unsupported claims before outreach |
Step-by-step setup
- Start with 10 representative rows before filling down hundreds.
- Keep raw source and export fields unchanged so you can audit the AI output.
- Run one formula to create a research summary, then inspect weak rows.
- Add constraints: max length, required format, and what to do when data is missing.
- Add a QA formula that flags missing facts and unsupported assumptions.
- Fill down once the prompt works on your sample rows.
How a Sheets workflow compares with Coresignal
GPT for Sheets adds AI research, scoring, and personalization directly in the spreadsheet, working on company lists you have already sourced. It does not ship a firmographic dataset - it interprets the source text you provide and marks what is unknown. It does not ship a proprietary database, so pair it with your own sourced data when you need verified contact fields. It is not affiliated with Coresignal and is not a drop-in replacement for every feature; the comparison here is factual and non-defamatory.
Use cases
- Account research: turn company lists into reviewable summaries.
- Prioritization: score and label accounts before reps invest time.
- Decision-maker context: summarize role, seniority, and likely priorities.
- Personalization: draft openers grounded in a specific signal.
- QA: flag rows missing evidence or making unsupported claims.
Best for / not best for
Best for: teams that already keep company lists in Google Sheets and want a lightweight, reviewable way to summarize, score, and prioritize accounts without licensing a large dataset.
Not best for: teams whose core need is a licensed firmographic or headcount dataset at scale; in that case use GPT for Sheets as the research and prioritization layer on top of data sourced elsewhere.
The strongest use case is enriching and prioritizing lists you already control. GPT for Sheets supplies the AI research and QA layer; you supply lawful, sourced data.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Domain to company info in Google Sheets
- Account research automation in Sheets
- Best AI model for lead enrichment in Sheets
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft. Use lawful public and business data only, do not rely on GPT for Sheets to reproduce a proprietary database, keep source columns visible, store source URLs or dates when relevant, and verify data before outreach. Follow consent, deliverability, and local compliance rules.
Frequently Asked Questions
Is GPT for Sheets a Coresignal replacement?
Not exactly. Coresignal licenses firmographic and employee datasets; GPT for Sheets is a spreadsheet-native AI layer for research, scoring, and personalization on lists you already have. It is a lighter alternative for spreadsheet-first teams and is unaffiliated with Coresignal.
Does it provide headcount or firmographic data?
No. GPT for Sheets does not include a proprietary dataset. It summarizes and scores from the source text you provide and marks unknowns rather than inventing figures like headcount.
What does it replace and what does it not?
It replaces the manual research, prioritization, and personalization work; it does not replace a licensed dataset. Pair it with sourced firmographic data when you need verified fields.
Should I trust every AI output automatically?
No. Treat output as a structured draft and use QA columns to flag missing evidence, unsupported claims, and rows that need manual verification.
Try a Coresignal alternative workflow in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and run these formulas on the lists you already have.