ben daamer This workflow corresponds to n8n.io template #13763 — we link there as the canonical source.
The workflow JSON
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{
"id": "CrPyRThN2WX2rP5s",
"name": "Score B2B leads with AI from webhook or form submissions",
"tags": [],
"nodes": [
{
"id": "9ea04987-285e-450e-a8e9-d95c956ccb68",
"name": "Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
608,
-128
],
"parameters": {
"color": 5,
"width": 480,
"height": 380,
"content": "## Score B2B Leads with AI\n\n**Who it's for:** Sales teams, freelancers, and agencies receiving leads via webhook or contact forms.\n\n**What it does:** Receives lead data (name, email, company, website), uses OpenAI to score each lead 1\u201310 based on fit, and routes to **Hot** (\u22657 \u2192 immediate follow-up) or **Nurture** (add to sequence).\n\n**Setup:** Add your OpenAI API key via pre-configured credentials in the AI Score Lead node. No hardcoded keys. Activate the workflow and test with Manual or send a POST to the webhook URL."
},
"typeVersion": 1
},
{
"id": "9003ee31-df43-4b1c-b2cd-3cc9dc084202",
"name": "Sticky Note 1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1152,
-336
],
"parameters": {
"color": 7,
"height": 140,
"content": "**Step 1 \u2014 Triggers**\n\n\u2022 Webhook: POST to `/incoming-lead`\n\u2022 Manual: Test with sample data"
},
"typeVersion": 1
},
{
"id": "b905b0c6-6080-43a8-98ca-25c4c9264164",
"name": "Sticky Note 2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1616,
-320
],
"parameters": {
"color": 7,
"width": 280,
"height": 120,
"content": "**Step 2\u20134 \u2014 Process**\n\nNormalize \u2192 AI Score (OpenAI) \u2192 Parse"
},
"typeVersion": 1
},
{
"id": "017f10ca-70ab-4de5-bf26-b4597ba22e52",
"name": "Sticky Note 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
2160,
-320
],
"parameters": {
"color": 7,
"width": 320,
"height": 120,
"content": "**Step 5 \u2014 Route**\n\nHot (\u22657) \u2192 immediate follow-up | Nurture \u2192 add to sequence"
},
"typeVersion": 1
},
{
"id": "51751392-d4ee-47f7-aa2a-af98b19033cd",
"name": "Config",
"type": "n8n-nodes-base.set",
"position": [
1184,
16
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "SCORE_THRESHOLD",
"type": "number",
"value": 7
}
]
}
},
"typeVersion": 3.4
},
{
"id": "b1b1561e-d014-4939-a078-23fabac0ed36",
"name": "Route by Source",
"type": "n8n-nodes-base.if",
"position": [
1408,
16
],
"parameters": {
"options": {},
"conditions": {
"options": {
"caseSensitive": true
},
"combinator": "and",
"conditions": [
{
"id": "1",
"operator": {
"type": "boolean",
"operation": "equals"
},
"leftValue": "={{ $json.body !== undefined && $json.body !== null }}",
"rightValue": true
}
]
}
},
"typeVersion": 2
},
{
"id": "c878ffac-d4a7-4f53-9f3e-9a01d6fedcee",
"name": "Manual Test",
"type": "n8n-nodes-base.manualTrigger",
"position": [
1184,
176
],
"parameters": {},
"typeVersion": 1
},
{
"id": "05e009ce-a50c-49a3-b229-dab5612c0e8a",
"name": "Sample Data",
"type": "n8n-nodes-base.set",
"position": [
1696,
224
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "name",
"type": "string",
"value": "Marie Dupont"
},
{
"id": "2",
"name": "email",
"type": "string",
"value": "user@example.com"
},
{
"id": "3",
"name": "company",
"type": "string",
"value": "Innovate SAS"
},
{
"id": "4",
"name": "website",
"type": "string",
"value": "https://innovate.co"
},
{
"id": "5",
"name": "SCORE_THRESHOLD",
"type": "number",
"value": "={{ $json.SCORE_THRESHOLD ?? 7 }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "e134a073-3ab5-4ce1-9043-b93238cd7c10",
"name": "Webhook Receive Lead",
"type": "n8n-nodes-base.webhook",
"position": [
1184,
-176
],
"parameters": {
"path": "incoming-lead",
"options": {},
"httpMethod": "POST",
"responseMode": "lastNode"
},
"typeVersion": 2.1
},
{
"id": "2523c78d-8eb2-47a2-9a58-40658d77b5db",
"name": "Normalize Lead Data",
"type": "n8n-nodes-base.set",
"position": [
1616,
-64
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "name",
"type": "string",
"value": "={{ $json.body?.name ?? $json.name ?? 'Lead' }}"
},
{
"id": "2",
"name": "email",
"type": "string",
"value": "={{ $json.body?.email ?? $json.email ?? '' }}"
},
{
"id": "3",
"name": "company",
"type": "string",
"value": "={{ $json.body?.company ?? $json.company ?? '' }}"
},
{
"id": "4",
"name": "website",
"type": "string",
"value": "={{ $json.body?.website ?? $json.website ?? '' }}"
},
{
"id": "5",
"name": "SCORE_THRESHOLD",
"type": "number",
"value": "={{ $json.SCORE_THRESHOLD ?? 7 }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "aae6950d-861a-41ed-a579-ac612d99b2cd",
"name": "AI Score Lead",
"type": "n8n-nodes-base.httpRequest",
"position": [
1760,
-64
],
"parameters": {
"url": "https://api.openai.com/v1/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"gpt-4o-mini\",\n \"messages\": [{\n \"role\": \"user\",\n \"content\": \"Score this B2B lead from 1-10. Reply with ONLY the number.\\n\\nName: {{ $json.name }}\\nEmail: {{ $json.email }}\\nCompany: {{ $json.company }}\\nWebsite: {{ $json.website }}\"\n }],\n \"temperature\": 0.3,\n \"max_tokens\": 10\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi"
},
"typeVersion": 4.2
},
{
"id": "7326f10a-a44c-4cd9-98ca-378d3c740dac",
"name": "Parse Score",
"type": "n8n-nodes-base.code",
"position": [
1904,
-32
],
"parameters": {
"jsCode": "const resp = $input.first().json;\nconst content = resp.choices?.[0]?.message?.content ?? resp.message?.content ?? '5';\nconst score = Math.min(10, Math.max(1, parseInt(String(content).match(/\\d+/)?.[0] || '5') || 5));\nlet lead = {};\ntry { lead = $('Normalize Lead Data').first().json; } catch (_) {}\nif (!lead || Object.keys(lead).length === 0) { try { lead = $('Sample Data').first().json; } catch (_) {} }\nconst threshold = lead.SCORE_THRESHOLD ?? 7;\nreturn { json: { ...lead, score, status: score >= threshold ? 'hot' : 'nurture', action: score >= threshold ? 'immediate_followup' : 'add_to_sequence', processed: true } };"
},
"typeVersion": 2
},
{
"id": "a260869e-574a-4771-8fd5-dc9afc96d42d",
"name": "Is Hot Lead?",
"type": "n8n-nodes-base.if",
"position": [
2128,
-32
],
"parameters": {
"options": {},
"conditions": {
"options": {
"caseSensitive": true
},
"combinator": "and",
"conditions": [
{
"id": "1",
"operator": {
"type": "number",
"operation": "gte"
},
"leftValue": "={{ $json.score }}",
"rightValue": "={{ $json.SCORE_THRESHOLD ?? 7 }}"
}
]
}
},
"typeVersion": 2
},
{
"id": "868d32ba-fa5e-4756-b938-ffe7c935810a",
"name": "Hot Lead",
"type": "n8n-nodes-base.set",
"position": [
2336,
-128
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "status",
"type": "string",
"value": "hot"
},
{
"id": "2",
"name": "action",
"type": "string",
"value": "immediate_followup"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "a5dbb376-57f2-4b0f-8849-d31dda75b6fb",
"name": "Nurture Lead",
"type": "n8n-nodes-base.set",
"position": [
2336,
80
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "status",
"type": "string",
"value": "nurture"
},
{
"id": "2",
"name": "action",
"type": "string",
"value": "add_to_sequence"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2b19f883-5abc-4ade-8bb3-62a0522bd05e",
"name": "Merge Output",
"type": "n8n-nodes-base.merge",
"position": [
2560,
-32
],
"parameters": {},
"typeVersion": 3
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"availableInMCP": false,
"executionOrder": "v1"
},
"versionId": "0e1a53b6-814a-46c2-a728-a764c87b49df",
"connections": {
"Config": {
"main": [
[
{
"node": "Route by Source",
"type": "main",
"index": 0
}
]
]
},
"Hot Lead": {
"main": [
[
{
"node": "Merge Output",
"type": "main",
"index": 0
}
]
]
},
"Manual Test": {
"main": [
[
{
"node": "Config",
"type": "main",
"index": 0
}
]
]
},
"Parse Score": {
"main": [
[
{
"node": "Is Hot Lead?",
"type": "main",
"index": 0
}
]
]
},
"Sample Data": {
"main": [
[
{
"node": "AI Score Lead",
"type": "main",
"index": 0
}
]
]
},
"Is Hot Lead?": {
"main": [
[
{
"node": "Hot Lead",
"type": "main",
"index": 0
}
],
[
{
"node": "Nurture Lead",
"type": "main",
"index": 0
}
]
]
},
"Nurture Lead": {
"main": [
[
{
"node": "Merge Output",
"type": "main",
"index": 1
}
]
]
},
"AI Score Lead": {
"main": [
[
{
"node": "Parse Score",
"type": "main",
"index": 0
}
]
]
},
"Route by Source": {
"main": [
[
{
"node": "Normalize Lead Data",
"type": "main",
"index": 0
}
],
[
{
"node": "Sample Data",
"type": "main",
"index": 0
}
]
]
},
"Normalize Lead Data": {
"main": [
[
{
"node": "AI Score Lead",
"type": "main",
"index": 0
}
]
]
},
"Webhook Receive Lead": {
"main": [
[
{
"node": "Config",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
• Receives lead data (name, email, company, website) from a webhook or manual trigger • Normalizes the payload and sends it to OpenAI for scoring (1–10) • Routes leads to Hot (≥7, immediate follow-up) or Nurture (add to sequence) • Output includes score, status, and action…
Source: https://n8n.io/workflows/13763/ — original creator credit. Request a take-down →
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