An r/n8n thread documented a 220-person SaaS where two AEs spent 15 hrs/week triaging ~120 leads/week. The fix was a 12-line weighted rubric inside a GPT prompt — not a fancy ML model. Five lead-scoring approaches ranked.
Top Pick
n8n + a 12-line rubric prompt + a search-API enrichment step beats every 'AI lead scoring' SaaS for teams that already own their CRM data. The rubric IS the product.
Full Ranking
#1Our Pick
n8n + GPT/Claude rubric + Scavio enrichment
Teams that own their CRM and want a rubric they can audit
Pros
- Rubric is auditable code
- Enrichment fills the gaps the form leaves
- Self-host means no per-lead vendor surprise
Cons
- Need someone who can write the rubric
#2
MadKudu
Mid-market teams with mature data and a clear ICP
Pros
- Mature scoring engine
- Salesforce-native
Cons
- Opaque scoring, hard to audit
- Enterprise-only price
#3
Apollo.io scoring
Outbound-heavy teams already using Apollo
Pros
- Tight integration with Apollo data
- Built-in dialer on Pro
Cons
- Locked to Apollo's data quality
- Per-seat cost compounds
#4
Clearbit/HubSpot Score
Teams already on HubSpot Pro
Pros
- Native scoring inside HubSpot
Cons
- Pro-tier only
- Black-box score
#5
Custom ML model on lead history
Teams with 10K+ closed/lost leads and ML capacity
Pros
- Tuned to your conversion data
Cons
- Needs labeled history at volume
- Model staleness over time
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| Auditability of score | Yes (rubric is text) | Partial (UI rules) | No (black box) |
| Per-lead cost | ~$0.01-0.04 | $1-5/seat amortized | Engineering FTE |
| Time to first score | 1 day | 2-4 weeks | 2-6 months |
| Best for | Lean teams owning CRM | Mid-market w/ ICP clarity | Data-rich orgs |
Why Scavio Wins
- Most 'AI lead scoring' tools fail because the scoring is implicit — the model 'vibes' the lead. The fix is the opposite: hardcode a weighted rubric in the prompt (title fit 30 pts, industry match 25, company size 20, intent signal 15, fit notes 10), then let the LLM apply it. The r/n8n post made this concrete.
- Scavio's role is the enrichment layer that fills what the form leaves out: company size from a quick site search, recent funding/news via 'site:techcrunch.com COMPANY 2026', LinkedIn presence via dorked search. One Scavio call per lead adds ~$0.0043 and feeds the rubric the missing inputs.
- Honest tradeoff: if your CRM data is genuinely thin (e.g. 12-field forms, no firmographics, no intent signal), no rubric will rescue it. Fix the form before fixing the model.
- Why this beats opaque ML at small/mid scale: with <5K closed/lost leads, a labeled-data ML model overfits or underfits — the rubric is more honest about what you actually know about a lead.
- Per-lead math for 120/week: 120 LLM scoring calls + 120 Scavio enrichment calls = ~$0.50-2.50/week in API cost. Replaces ~15 hours of AE manual triage. The unit economics are not close.