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Lab build: an n8n pipeline that scores and routes inbound leads automatically
A portfolio lab build on Tessera (a fictional SaaS): an importable n8n workflow that enriches, scores, logs, and routes inbound B2B leads, with cost and latency measured on real runs.
- Role
- Solo build (no-code workflow)
- Stack
- n8n · OpenAI gpt-5-nano · Google Sheets · Slack
The problem
Inbound B2B leads pile up faster than a sales team can triage them, and the good ones go cold while someone manually researches and sorts the noise.
The approach
- Built an importable n8n workflow: a lead form POSTs to a webhook, and the flow enriches each lead with a single GET of their homepage (best-effort context, not scraping).
- Asked gpt-5-nano to return strict JSON (a 1–10 score, a hot/warm/cold tier, and a two-sentence reason), then normalized and clamped it so a malformed response degrades to score 0 instead of throwing.
- Logged every lead to a Google Sheets CRM and alerted sales on Slack for hot leads only (score ≥ 8).
- Shipped five realistic sample payloads across the tiers so the workflow can be tested end-to-end right after import.
The results
A no-code, no-host lead pipeline that qualifies and routes every inbound lead automatically. Reps start the day with a triaged queue instead of a raw inbox. Throughput and per-lead cost are measured on real runs, never invented.
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