Copy-paste formulas for PE deal sourcing 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.
Target company summary
A: company · B: source notes · C: thesis
=GPT("Summarize this potential target for a deal team: " & A2 & ". Source notes: " & B2 & ". Investment thesis: " & C2 & ". Return a concise business summary, likely segment, ownership signal, and relevance to the thesis. Mark anything not stated as Unknown and recommend verification.")
Ownership / size signal
A: company · B: source text
=GPT("From this company and source text, identify ownership and size signals (founder-owned, PE-backed, headcount band, revenue hints): " & A2 & ". Source: " & B2 & ". Return the signals, the evidence, and confidence. These are estimates, not verified financials.")
Thesis-fit score 1-5
A: company · B: thesis criteria · C: source text
=GPT("Score this target 1-5 for thesis fit. Company: " & A2 & ". Thesis criteria: " & B2 & ". Source text: " & C2 & ". Return score, reason, confidence, and what to verify manually. This is a research aid, not investment advice.")
Owner / contact note
A: company · B: notes
=GPT("From these notes about " & A2 & ": " & B2 & ", summarize the likely owner or key contact and a respectful outreach angle for a sourcing conversation. Mark unknowns clearly and keep it factual.")
Short answer
A Clay alternative for private-equity deal sourcing in Google Sheets is a spreadsheet-native way to research and prioritize targets without adopting a heavy GTM stack. Instead of moving rows into a separate tool, GPT for Sheets runs prompts across your target universe to produce company summaries, ownership signals, thesis-fit scores, and contact notes in adjacent columns. Outputs are research aids, not investment advice or verified financials.
Fastest path: Install GPT for Sheets → add your source columns → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for PE, search-fund, and corporate-development associates who build and enrich target lists for platform searches and add-on acquisitions and already keep them in spreadsheets.
Workflow
A practical sheet for this workflow usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Target company | Stable row anchor for each target |
| B | Source notes: website, filings, press, directory | Keeps AI grounded in inspectable evidence |
| C | Investment thesis or criteria | Sharpens relevance and scoring |
| D | Segment, geography, or sub-sector | Filters the universe to your mandate |
| E | AI research summary | First useful interpretation of the row |
| F | Thesis-fit score and label | Sorts the universe for review |
| G | Owner/contact note or next action | Turns research into a sourcing step |
| H | QA flag | Stops unsupported claims before review |
Step-by-step setup
- Start with 10 representative rows before filling down hundreds.
- Keep raw source 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, and verify every deal-relevant fact manually.
Why these teams compare this with Clay
Clay is a powerful enrichment platform, but many deal teams already maintain large target sheets and do not want another standalone GTM workspace. GPT for Sheets is positioned for teams that want a spreadsheet-native way to turn a target universe into summaries, ownership signals, thesis-fit scores, and contact notes. It is not affiliated with Clay; Clay and other third-party product names are trademarks of their respective owners, and comparisons here are factual and non-defamatory.
Use cases
- Market maps: turn a long universe into reviewable company summaries.
- Ownership signals: estimate founder-owned vs backed and rough size from public evidence.
- Thesis scoring: rank targets against your criteria with a reason and confidence.
- Add-on sourcing: triage roll-up candidates around a platform.
- QA: flag rows with missing evidence or unsupported claims.
Best for / not best for
Best for: PE, search-fund, and corp-dev teams that maintain target lists in Google Sheets and want reviewable AI research and thesis-fit triage across a large universe.
Not best for: teams that need verified financials, audited data, or investment recommendations from the tool; those require licensed data and human diligence.
The strongest use case is turning a long universe into prioritized, reviewable rows. GPT for Sheets supplies the research and triage layer; your team supplies verified data and judgment.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Private-equity deal sourcing in Sheets
- PE add-on acquisition research in Sheets
- Market-map research in Sheets
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft and a research aid, not investment advice or verified financials. Use lawful public and business data only, keep source columns visible, store source URLs or dates, and require human verification of all deal-relevant data before acting. Do not infer sensitive attributes. Follow your firm’s compliance and confidentiality rules.
Frequently Asked Questions
What is the fastest way to start deal sourcing in Sheets?
Install GPT for Sheets, add columns for the company, source notes, and thesis, paste one formula into row 2, review the output, then fill it down once it works on sample rows.
Is this really a Clay alternative for private equity?
For spreadsheet-first deal teams, yes: GPT for Sheets provides Clay-style research, scoring, and summarization directly in Google Sheets. It is not affiliated with Clay and does not replace licensed data or diligence.
Does it provide verified financials or investment advice?
No. Outputs are AI research aids based on the evidence you provide, not verified financials or investment advice. Verify every deal-relevant fact manually.
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 research.
Start deal sourcing in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and run these formulas where your target lists already live.