Waterfall Enrichment in Google Sheets Without Clay

Run this workflow across every row Install GPT for Sheets to enrich, score, draft, and review this workflow inside Google Sheets.

Install GPT for Sheets See pricing

Copy-paste GPT for Sheets formulas

Paste these into row 2, adapt column letters to your sheet, then fill down after reviewing sample output.

Research summary

A: account/lead · B: domain/source notes

=GPT("Choose the best available enrichment value from columns B:D and explain which source was used. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

Score and prioritize

A: account · C: research notes · D: segment

=GPT("If the target field is missing, create a manual-research task instead of guessing. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

Draft or review output

A: account · C: AI output · E: compliance/review notes

=GPT("Review this enriched row for conflicting data and assign confidence high, medium, or low. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

Create a QA review column

A: source row · F: final draft

=GPT("Review this row for unsupported claims, missing sources, and compliance concerns. Source: " & A2 & " Draft: " & F2)

Short answer

You can build a lightweight enrichment waterfall in Google Sheets by trying source columns in order, asking GPT for Sheets to fill structured gaps, and adding confidence/QA fields before export. It is designed for B2B sales teams, RevOps-light teams, agencies, and founders evaluating Clay-style enrichment who need useful row-by-row output without moving every list into another workspace.

Use it when your source of truth is already a spreadsheet: exports from a CRM, event list, directory, marketplace, ATS, service system, or hand-built prospect list. The workflow is simple: keep raw source columns intact, add AI output columns, add confidence and review fields, then export only approved rows.

Trademark note: Clay is a third-party trademark. DocGPT.AI and GPT for Sheets are not affiliated with, endorsed by, or sponsored by Clay. This page compares workflow fit without claiming feature or price parity.

Workflow

A practical sheet for this use case usually starts with these source columns:

Inputs: company, domain, existing email/source data, enrichment source 1, source 2, source 3, target field.
AI output columns: best available value, source used, confidence, gap note, manual-review flag.
Review columns: confidence, missing facts, owner, approval status, and next action.

Recommended process:

Import or paste the raw list into Google Sheets and freeze the source columns.
Add one narrow GPT for Sheets formula per task: research summary, score, personalization, or QA.
Run the formulas on 10-20 representative rows before filling down.
Tighten prompts so the model returns concise, structured fields instead of broad strategy.
Review low-confidence rows manually and keep an audit trail before CRM import, email drafting, or sales handoff.

Copy-paste formulas

The formula cards above are ready to adapt. Here are the core formulas in plain text for quick copying:

=GPT("Choose the best available enrichment value from columns B:D and explain which source was used. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

=GPT("If the target field is missing, create a manual-research task instead of guessing. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

=GPT("Review this enriched row for conflicting data and assign confidence high, medium, or low. Account: " & A2 & " Context: " & B2 & " Notes: " & C2)

=GPT("Review this row for unsupported claims, missing sources, and compliance concerns. Source: " & A2 & " Draft: " & F2)

For better output, ask for a strict format such as Score:, Reason:, Missing facts:, and Next action:. If a row lacks enough context, tell the model to return Needs manual research rather than inventing details.

Best fit

Best for: teams that want a practical Sheets-native version of enrichment waterfalls before adopting heavier tooling.

Not best for: enterprise-scale data orchestration, complex API routing, or automated vendor waterfalling across many paid data sources.

This is where GPT for Sheets is strongest: lightweight, transparent, and easy to iterate. You can see the source cells, prompt, AI answer, and reviewer status in one row. That makes it easier to coach the team, spot hallucinations, and decide which columns deserve more data.

Use cases

Build an account or lead research column before sales outreach.
Score rows by ICP fit, urgency, or workflow relevance.
Generate first-draft personalization that a human can approve.
Normalize messy list fields before CRM, ATS, ecommerce, or campaign import.
Create a QA column that flags unsupported claims, missing context, or compliance risks.

Quality control

Follow third-party data-source terms and do not invent missing fields. Mark unknowns clearly.

Before using the output externally:

Verify facts that affect prospects, customers, candidates, listings, accounts, or revenue.
Do not infer sensitive or protected attributes.
Keep generated copy separate from approved copy.
Add a reviewer column for high-value or regulated workflows.
Use /gpt-for-sheets/ for setup and /gpt-for-sheets/#pricing when you are ready to process larger lists.

FAQ

What is waterfall enrichment?

It is a process of trying multiple sources in priority order until a field is filled with acceptable confidence.

Can Google Sheets replace Clay for waterfalls?

For lightweight workflows, yes. For complex API orchestration and large operations, a dedicated platform may still be better.

Should AI guess missing data?

No. Prompt it to return unknown or a manual research task when sources do not support a field.