Apollo List Quality Scorer
Score and clean prospect lists before you sequence them.
27-node n8n workflow that scores and cleans prospect lists before sequencing. 4 agents: Fetcher (code-only, Apollo API enrichment), Researcher (the analysis model + web_search for thin data), Analyst (the analysis model, LQS 5-criteria weighted rubric), Formatter (code-only, Google Sheets output). 3-way routing: pursue >= 7, enrich 4-6, remove < 4. $0.024/prospect. Blueprint Quality Standard (BQS) v2 certified. We built this after a team imported an Apollo list of 5,000 contacts and found that 1,200 had invalid emails and 800 had left their companies. The scorer grades list quality before you waste outreach credits.
Last updated March 15, 2026
B2B sales teams spend 65% of their time on non-selling activities, with manual lead qualification consuming the largest share. As contact databases grow past 10K records and buying committees expand to 6-10 stakeholders, human-only qualification cannot keep pace. Automated scoring with transparent, auditable criteria lets reps focus on conversations instead of spreadsheets.
Four Agents. Five Quality Signals. Scored Lists Before Sequencing.
Step 1 — Fetcher
Webhook + Code
Webhook accepts JSON array or CSV payload containing prospect lists. Fetcher parses the input, normalizes field names, and calls Apollo.io People Enrichment API per prospect — email verification status, company data, technology stack, headcount, industry, and LinkedIn URL. Thin data detection flags prospects missing key fields for downstream web_search enrichment.
Step 2 — Researcher
Tier 2 Classification
What does Researcher actually decide? the analysis model + web_search activates only for thin Apollo data — prospects where Apollo returned incomplete company or contact information. Enriches missing fields via web search: company website, headcount range, industry classification, recent news. Rich Apollo records skip this step entirely, keeping cost at $0 for well-populated prospects.
Step 3 — Analyst
Tier 2 Classification
This step exists because raw data alone is not enough. the analysis model scores each prospect across 5 weighted LQS criteria: icp_fit (30%), data_completeness (20%), deliverability_signals (20%), enrichment_quality (15%), recency (15%). Per-criteria scoring 0–10 with evidence. Weighted composite LQS drives 3-way routing: LQS ≥ 7 pursue, 4–6 enrich, < 4 remove. Each score includes reasoning summary.
Step 4 — Formatter
Code + 3-way Route
Without this step, upstream analysis sits idle. Routes based on LQS composite score. Writes annotated Google Sheets output with 16 columns: prospect data, per-criteria scores, LQS composite, action tag (pursue/enrich/remove), and reasoning summary. Summary stats returned via webhook response: total processed, pursue/enrich/remove counts, average LQS, and estimated cost.The Researcher costs more than the Judge — web search injects 30K-40K tokens per call. That is why we publish ITP-measured costs, not estimates.
That's the full pipeline. Here's what it intentionally does NOT do — and why those boundaries exist.
What It Does NOT Do
Does not build prospect lists from scratch — that is what Outbound Prospecting Agent does
Does not send outreach emails — scores and annotates lists for your sequencer
Does not create CRM records — outputs to Google Sheets for flexible downstream use
Does not monitor deal stages — that is what Deal Intelligence Agent does
Does not score existing CRM contacts — that is what Contact Re-Engagement Scorer does
Does not verify email addresses — relies on Apollo.io verification status as an input signal
With those boundaries clear, here's everything that ships when you purchase.
The Complete Customer Success Bundle
6 files.
The technical specifications below are ITP-measured, not estimated.
Tested. Measured. Documented.
Every metric is Independent Test Protocol (ITP)-measured. The Apollo List Quality Scorer validates prospect lists before sequencing — enriching via Apollo.io API, scoring across 5 weighted quality criteria with evidence, and routing to pursue/enrich/remove at $0.024/prospect.
Workflow Nodes
27
Blueprint Quality Standard
12/12 PASS
Agent Architecture
4 — Fetcher (code-only) → Researcher (Sonnet 4.6) → Analyst (Sonnet 4.6) → Formatter (code-only)
Required Credentials
3 — Anthropic API, Apollo.io (httpHeaderAuth), Google Sheets (OAuth2)
Bundle Contents
9 files
Cost per Prospect
$0.024/prospect (ITP-measured)
ITP Milestones
14/14 PASS
n8n Compatibility
2.11.2+
Tested on n8n v2.7.5, March 2026
Apollo List Quality Scorer v1.0.0 — Technical Reference━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Architecture: 27 n8n nodes, 4 agents (Fetcher → Researcher → Analyst → Formatter)Trigger: Webhook (JSON array or CSV payload)Input: Prospect list — names, emails, companies, titlesIntelligence: Sonnet 4.6 (Researcher web_search + Analyst LQS scoring)Output: Google Sheets (16-column annotated output) + Webhook responseCost: $0.024/prospect (ITP-measured average)ITP: 20 records, 14/14 milestones PASS, LQS range 0.45–9.37BQS: 12/12 PASSTool A: Apollo.io (input — People Enrichment API)Tool B: Google Sheets (output — annotated scored list)Intelligence: LQS 5-criteria weighted + 3-way routing (pursue/enrich/remove)Cost Value: 0.024
What You'll Need
⚠ Data Source Dependency
Researcher uses Anthropic web_search for thin Apollo data only. Rich Apollo records skip web_search entirely.
Platform
n8n 2.11.2+
Est. Monthly API Cost
$24/month (1,000 prospects/month)
Credentials Required
- ▪Anthropic API
- ▪Apollo.io (httpHeaderAuth)
- ▪Google Sheets (OAuth2)
Services
- ▪Apollo.io account (People Enrichment API)
- ▪Google Workspace (Sheets access)
Setup Track
Quick Start
~15 min
All credentials live, n8n running
Full Setup
1–2 hrs
Needs API config + tables
From Scratch
2–4 hrs
No n8n, no credentials
Apollo List Quality Scorer v1.0.0
$199
one-time purchase
What you get:
- ✓ITP-tested 27-node n8n workflow — import and deploy
- ✓Webhook input accepts JSON array or CSV payload for batch processing
- ✓Apollo.io People Enrichment API per prospect — email verification, company data, tech stack
- ✓LQS 5-criteria weighted scoring: icp_fit (30%), data_completeness (20%), deliverability_signals (20%), enrichment_quality (15%), recency (15%)
- ✓Per-criteria scoring (0–10) with evidence-based assessment and reasoning summary
- ✓3-way routing: LQS ≥ 7 pursue, 4–6 enrich, < 4 remove
- ✓Thin data detection: missing fields trigger Researcher web_search enrichment automatically
- ✓Annotated Google Sheets output with 16 columns (prospect data, scores, action tags)
- ✓Dual-the analysis model: $0.024/prospect all-in — no Opus required
- ✓ITP test results with 20 records, 14/14 milestones, LQS range 0.45–9.37
- ✓All sales final after download
Frequently Asked Questions
How does it differ from Outbound Prospecting Agent?+
Complementary products for different stages. Outbound Prospecting Agent (OPA) builds prospect lists from scratch via Apollo search and sends personalized outreach via Gmail. Apollo List Quality Scorer (ALQS) validates and scores existing lists you already have — from Apollo exports, purchased lists, or event attendee data — before you load them into a sequencer.
What are the five LQS criteria?+
ICP Fit (30%) — industry match, headcount range, geography alignment. Data Completeness (20%) — email presence and verification, phone, title, company coverage. Deliverability Signals (20%) — email verification status, catch-all detection, role-based flagging, domain reputation.
What does the 3-way routing do?+
LQS ≥ 7 tags the prospect as "pursue" — ready for sequencing. LQS 4–6 tags as "enrich" — worth keeping but needs additional data before outreach. LQS < 4 tags as "remove" — low quality, do not waste sequence slots.
When does the Researcher activate?+
Only for thin Apollo data — prospects where Apollo returned incomplete company or contact information. Rich Apollo records skip the Researcher entirely, keeping cost at $0 for those prospects. This selective enrichment pattern keeps the average cost at $0.024/prospect instead of paying for web_search on every record.
What input formats does it accept?+
Webhook accepts two formats: JSON array (matching Apollo API export structure) and CSV payload (comma-separated with header row). The Fetcher normalizes both formats into a standard internal schema before enrichment. Maximum batch size depends on n8n memory — tested with up to 500 prospects per batch.
Why Sonnet instead of Opus for scoring?+
LQS scoring is structured classification against a defined rubric — not open-ended reasoning. the analysis model handles this with high accuracy at $3/$15 per million tokens vs Opus at $15/$75. Dual-Sonnet architecture keeps cost at $0.024/prospect.
What does the Google Sheets output look like?+
16 columns: prospect name, email, title, company, industry, headcount, email verification status, 5 per-criteria scores (0–10), LQS composite, action tag (pursue/enrich/remove), reasoning summary, and data source (Apollo/web_search/both). One row per prospect, sorted by LQS descending.
Does it use web scraping?+
Partially. The Researcher uses Anthropic web_search for thin Apollo data only — prospects where Apollo returned incomplete information. Rich Apollo records skip web_search entirely.
Is there a refund policy?+
All sales are final after download. Review the Blueprint Dependency Matrix and prerequisites before purchase. Questions?
Related Blueprints
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Apollo-sourced leads, AI-qualified and personally emailed — zero manual prospecting.
Contact Re-Engagement Scorer
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Contact Intelligence Agent
Automated CRM enrichment that researches, scores, and writes back to Pipedrive — zero manual lookup.