BeyondPresence 销售机会评分 - 实时潜在客户智能
中级
这是一个Sales, AI领域的自动化工作流，包含 14 个节点。主要使用 Code, Slack, Switch, Webhook, RespondToWebhook 等节点，结合人工智能技术实现智能自动化。 BeyondPresence 销售智能 → 实时潜在客户评分
前置要求
- •Slack Bot Token 或 Webhook URL
- •HTTP Webhook 端点（n8n 会自动生成）
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "3lIutQSsxmpnS38v",
"meta": {
"instanceId": "eb3d53320c110bef9b66cf21b0da1ce60c7b6876e22315eca1be511a26fd726c",
"templateCredsSetupCompleted": true
},
"name": "BeyondPresence 销售机会评分 - 实时潜在客户智能",
"tags": [],
"nodes": [
{
"id": "8131b77c-66b2-4138-bac9-1062dd50b631",
"name": "工作流概览",
"type": "n8n-nodes-base.stickyNote",
"position": [
80,
-600
],
"parameters": {
"color": 6,
"width": 560,
"height": 660,
"content": "## 🎯 销售机会评分系统"
},
"typeVersion": 1
},
{
"id": "ab3a8f0b-a1ab-4f88-80ed-435115915cb3",
"name": "评分规则",
"type": "n8n-nodes-base.stickyNote",
"position": [
680,
-600
],
"parameters": {
"color": 4,
"width": 400,
"height": 780,
"content": "## 📊 评分逻辑"
},
"typeVersion": 1
},
{
"id": "6395e121-1f83-4d07-a8e2-1566c37511ce",
"name": "提醒系统",
"type": "n8n-nodes-base.stickyNote",
"position": [
1100,
-600
],
"parameters": {
"color": 3,
"width": 380,
"height": 780,
"content": "## 🔔 Slack 提醒"
},
"typeVersion": 1
},
{
"id": "abc9e5e4-df53-4891-97ff-6c18fd06d534",
"name": "BeyondPresence 实时 Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
100,
-120
],
"webhookId": "beyondpresence-sales-intelligence",
"parameters": {
"path": "beyondpresence-sales-intelligence",
"options": {
"rawBody": false
},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "f94cb19d-0bca-4f42-8f25-11cc853f3323",
"name": "确认接收",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
280,
-120
],
"parameters": {
"options": {
"responseCode": 200
},
"respondWith": "json",
"responseBody": "{\n \"status\": \"success\",\n \"message\": \"Sales intelligence processing\",\n \"timestamp\": \"{{ $now.toISO() }}\"\n}"
},
"typeVersion": 1.2
},
{
"id": "9dae992f-6930-4db1-9c18-b85deaa352df",
"name": "筛选用户消息",
"type": "n8n-nodes-base.switch",
"position": [
460,
-120
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "user_message",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event_type }}",
"rightValue": "message"
},
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.message.sender }}",
"rightValue": "user"
}
]
},
"renameOutput": true
},
{
"outputKey": "call_ended",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event_type }}",
"rightValue": "call_ended"
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": "none"
}
},
"typeVersion": 3.2
},
{
"id": "c7cc875b-4282-4197-9d56-879a287fe385",
"name": "评分销售机会",
"type": "n8n-nodes-base.code",
"position": [
720,
-160
],
"parameters": {
"jsCode": "// Sales Opportunity Scoring Engine\n// Helper function for recommendations (moved to top for better organization)\nfunction generateRecommendations(score, signals) {\n const recommendations = [];\n \n if (score >= 70) {\n recommendations.push('📞 Schedule immediate follow-up call');\n recommendations.push('👤 Loop in senior sales rep');\n recommendations.push('📊 Prepare custom pricing proposal');\n } else if (score >= 40) {\n recommendations.push('📧 Send detailed product information');\n recommendations.push('📅 Offer demo scheduling link');\n recommendations.push('📚 Share relevant case studies');\n } else if (score < 0) {\n recommendations.push('🎯 Address specific objections');\n recommendations.push('💡 Focus on value proposition');\n recommendations.push('📞 Schedule follow-up for better timing');\n }\n \n if (signals.includes('🏁 Competitor Evaluation')) {\n recommendations.push('⚔️ Prepare competitive differentiation materials');\n }\n \n return recommendations;\n}\n\ntry {\n // Safely extract input data with validation\n const input = $input.first();\n if (!input || !input.json || !input.json.body) {\n throw new Error('Invalid input structure');\n }\n \n const messageData = input.json.body;\n const message = messageData.message;\n const callId = messageData.call_id || 'unknown';\n const userName = messageData.call_data?.userName || 'Unknown User';\n \n // Validate message structure\n if (!message || !message.message) {\n throw new Error('Message content is missing');\n }\n \n // Initialize scoring\n let opportunityScore = 0;\n const detectedSignals = [];\n const alerts = [];\n \n // Buying Signal Detection with Scoring\n const buyingSignals = {\n high_intent: {\n keywords: ['purchase', 'buy', 'implement', 'contract', 'agreement', 'sign up', 'ready to move forward'],\n score: 30,\n label: '🔥 High Purchase Intent'\n },\n decision_maker: {\n keywords: ['decision maker', 'my team', 'our company', 'we need', 'our budget', 'my boss', 'approval from'],\n score: 25,\n label: '👔 Decision Maker Indicator'\n },\n demo_request: {\n keywords: ['demo', 'trial', 'test', 'try it', 'see it in action', 'show me', 'walk through'],\n score: 25,\n label: '🖥️ Demo Request'\n },\n timeline: {\n keywords: ['when can', 'how soon', 'timeline', 'start date', 'implement by', 'deadline', 'need this by'],\n score: 20,\n label: '⏰ Timeline Discussion'\n },\n budget: {\n keywords: ['budget', 'cost', 'pricing', 'investment', 'roi', 'pay', 'afford', 'price point'],\n score: 15,\n label: '💰 Budget Discussion'\n },\n interest: {\n keywords: ['interested', 'tell me more', 'sounds good', 'like this', 'impressive', 'exactly what', 'perfect'],\n score: 15,\n label: '👍 Positive Interest'\n },\n problem_fit: {\n keywords: ['solve our problem', 'exactly what we need', 'perfect for us', 'this would help', 'addresses our'],\n score: 20,\n label: '✅ Strong Problem-Solution Fit'\n }\n };\n \n // Negative Signal Detection\n const negativeSignals = {\n price_objection: {\n keywords: ['too expensive', 'cant afford', 'over budget', 'too much', 'cheaper option', 'cost too high'],\n score: -25,\n label: '💸 Price Objection'\n },\n not_interested: {\n keywords: ['not interested', 'no thanks', 'not for us', 'pass', 'not looking', 'don\\'t need'],\n score: -30,\n label: '❌ Not Interested'\n },\n timing: {\n keywords: ['not right now', 'maybe later', 'next year', 'not a priority', 'down the road', 'future'],\n score: -15,\n label: '⏸️ Bad Timing'\n },\n just_browsing: {\n keywords: ['just looking', 'researching', 'comparing options', 'early stages', 'exploring', 'window shopping'],\n score: -10,\n label: '🔍 Just Browsing'\n }\n };\n \n // Competitor Detection \n const competitors = [\n 'competitor', 'alternative', 'other options', 'shopping around',\n 'comparing', 'versus', 'instead of', 'switch from',\n // Competitor Detection - Add your specific competitors here\n 'competitor1', 'competitor2'\n ];\n \n // Deal Size Indicators\n const dealSizeIndicators = {\n enterprise: {\n keywords: ['enterprise', 'company-wide', 'global', 'multiple teams', 'thousand users', 'all departments', 'entire organization'],\n multiplier: 10,\n label: '🏢 Enterprise Deal'\n },\n mid_market: {\n keywords: ['department', 'team of', 'hundred users', 'multiple locations', 'division', 'several teams'],\n multiplier: 5,\n label: '🏬 Mid-Market Deal'\n },\n small_business: {\n keywords: ['small team', 'few users', 'startup', 'small business', 'just us', 'handful of people'],\n multiplier: 1,\n label: '🏪 Small Business Deal'\n }\n };\n \n // Process message through scoring engine\n const messageLower = message.message.toLowerCase();\n \n // Check buying signals\n Object.entries(buyingSignals).forEach(([key, signal]) => {\n if (signal.keywords.some(keyword => messageLower.includes(keyword))) {\n opportunityScore += signal.score;\n detectedSignals.push(signal.label);\n }\n });\n \n // Check negative signals\n Object.entries(negativeSignals).forEach(([key, signal]) => {\n if (signal.keywords.some(keyword => messageLower.includes(keyword))) {\n opportunityScore += signal.score; // Note: score is negative\n detectedSignals.push(signal.label);\n alerts.push(`⚠️ Objection Detected: ${signal.label}`);\n }\n });\n \n // Check for competitors\n const mentionedCompetitors = competitors.filter(comp => \n messageLower.includes(comp.toLowerCase())\n );\n if (mentionedCompetitors.length > 0) {\n alerts.push(`🎯 Competitor Mentioned: ${mentionedCompetitors.join(', ')}`);\n detectedSignals.push('🏁 Competitor Evaluation');\n }\n \n // Estimate deal size\n let dealSizeEstimate = 'standard';\n let dealMultiplier = 2;\n Object.entries(dealSizeIndicators).forEach(([size, indicator]) => {\n if (indicator.keywords.some(keyword => messageLower.includes(keyword))) {\n dealSizeEstimate = size;\n dealMultiplier = indicator.multiplier;\n detectedSignals.push(indicator.label);\n }\n });\n \n // Determine lead temperature\n let leadTemperature = '🧊 Cold';\n let alertUrgency = 'low';\n if (opportunityScore >= 70) {\n leadTemperature = '🔥 Hot';\n alertUrgency = 'high';\n alerts.push('🚨 HOT LEAD DETECTED!');\n } else if (opportunityScore >= 40) {\n leadTemperature = '🌡️ Warm';\n alertUrgency = 'medium';\n } else if (opportunityScore >= 20) {\n leadTemperature = '😎 Cool';\n alertUrgency = 'low';\n }\n \n // Extract numbers for deal size estimation - Improved regex\n const numberMatches = messageLower.match(/\\$?\\d{1,3}(?:,\\d{3})*(?:\\.\\d{2})?[km]?|\\d+[km]/gi);\n let estimatedValue = null;\n if (numberMatches && numberMatches.length > 0) {\n // Convert to standardized number\n const lastNumber = numberMatches[numberMatches.length - 1];\n let parsedValue = 0;\n \n if (lastNumber.toLowerCase().includes('k')) {\n parsedValue = parseFloat(lastNumber.replace(/[^0-9.]/g, '')) * 1000;\n } else if (lastNumber.toLowerCase().includes('m')) {\n parsedValue = parseFloat(lastNumber.replace(/[^0-9.]/g, '')) * 1000000;\n } else {\n parsedValue = parseFloat(lastNumber.replace(/[$,]/g, ''));\n }\n \n // Only set estimatedValue if we got a valid number\n if (!isNaN(parsedValue) && parsedValue > 0) {\n estimatedValue = parsedValue * dealMultiplier;\n }\n }\n \n // Prepare output\n return [{\n json: {\n // Message context\n callId: callId,\n userName: userName,\n timestamp: message.sent_at || new Date().toISOString(),\n sender: message.sender || 'unknown',\n message: message.message,\n \n // Scoring results\n opportunityScore: opportunityScore,\n leadTemperature: leadTemperature,\n alertUrgency: alertUrgency,\n \n // Detected signals\n detectedSignals: detectedSignals,\n alerts: alerts,\n \n // Competitor intelligence\n competitorsMentioned: mentionedCompetitors,\n hasCompetitorMention: mentionedCompetitors.length > 0,\n \n // Deal estimation\n dealSizeCategory: dealSizeEstimate,\n dealMultiplier: dealMultiplier,\n estimatedDealValue: estimatedValue,\n formattedDealValue: estimatedValue ? `$${estimatedValue.toLocaleString()}` : 'Not detected',\n \n // Action recommendations\n recommendedActions: generateRecommendations(opportunityScore, detectedSignals),\n \n // Metadata\n processedAt: new Date().toISOString(),\n workflowExecution: $execution?.id || 'unknown',\n \n // Success indicator\n success: true,\n error: null\n }\n }];\n \n} catch (error) {\n // Return error response\n return [{\n json: {\n success: false,\n error: error.message,\n callId: 'error',\n userName: 'Unknown',\n timestamp: new Date().toISOString(),\n opportunityScore: 0,\n leadTemperature: '❌ Error',\n alertUrgency: 'high',\n alerts: [`🚨 Processing Error: ${error.message}`],\n recommendedActions: ['🔧 Check input data format', '📞 Contact technical support'],\n processedAt: new Date().toISOString()\n }\n }];\n}"
},
"typeVersion": 2
},
{
"id": "88281417-5a4a-414d-9c8f-295ac1b3aeaa",
"name": "路由提醒",
"type": "n8n-nodes-base.switch",
"position": [
920,
-200
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "hot_lead",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.alertUrgency }}",
"rightValue": "high"
}
]
},
"renameOutput": true
},
{
"outputKey": "competitor_alert",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.hasCompetitorMention }}",
"rightValue": "={{ true }}"
}
]
},
"renameOutput": true
},
{
"outputKey": "qualified_lead",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "number",
"operation": "gte"
},
"leftValue": "={{ $json.opportunityScore }}",
"rightValue": 40
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": "none"
}
},
"typeVersion": 3.2
},
{
"id": "0f6ed4eb-c8d4-4c1e-8c03-991d887c83f9",
"name": "Slack：热门潜在客户提醒",
"type": "n8n-nodes-base.slack",
"position": [
1260,
-260
],
"webhookId": "bb0d3a7c-7481-4a86-b52a-9511cdc2309e",
"parameters": {
"text": "=🔥 *HOT LEAD ALERT!*\n\n*Lead:* {{ $json.userName }}\n*Score:* {{ $json.opportunityScore }} {{ $json.leadTemperature }}\n*Call ID:* `{{ $json.callId }}`\n\n*Message:* _\"{{ $json.message }}\"_\n\n*Detected Signals:*\n{{ $json.detectedSignals.join('\\n') }}\n\n*Recommended Actions:*\n{{ $json.recommendedActions.join('\\n') }}\n\n{{ $json.estimatedDealValue ? `💰 *Estimated Deal Value:* ${$json.formattedDealValue}` : '' }}",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "name",
"value": "#sales-hot-leads"
},
"otherOptions": {
"mrkdwn": true
},
"authentication": "oAuth2"
},
"credentials": {
"slackOAuth2Api": {
"id": "fqQEQ6t5FN8BPrTs",
"name": "Slack account"
}
},
"typeVersion": 2.2
},
{
"id": "35d57ba3-432e-444a-bde4-b6bc5a340ee7",
"name": "Slack：竞争对手提醒",
"type": "n8n-nodes-base.slack",
"position": [
1240,
-120
],
"webhookId": "987e4ae2-0036-4cd1-8986-775c7ba988e7",
"parameters": {
"text": "=🎯 *Competitor Intelligence Alert*\n\n*Lead:* {{ $json.userName }}\n*Competitors Mentioned:* `{{ $json.competitorsMentioned.join(', ') }}`\n\n*Context:* _\"{{ $json.message }}\"_\n\n*Current Lead Score:* {{ $json.opportunityScore }} {{ $json.leadTemperature }}\n\n*Recommended Response:*\n⚔️ Emphasize unique differentiators\n📊 Share competitive comparison docs\n🎯 Address specific competitor weaknesses",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "name",
"value": "#sales-competitors"
},
"otherOptions": {
"mrkdwn": true
},
"authentication": "oAuth2"
},
"credentials": {
"slackOAuth2Api": {
"id": "fqQEQ6t5FN8BPrTs",
"name": "Slack account"
}
},
"typeVersion": 2.2
},
{
"id": "6ddcb61a-dc4d-4585-b0e2-e7c62eba7b3f",
"name": "Slack：合格潜在客户",
"type": "n8n-nodes-base.slack",
"position": [
1240,
20
],
"webhookId": "dfb87f01-c527-42dc-9d4c-296f83211373",
"parameters": {
"text": "=📈 *Qualified Lead Update*\n\n*Lead:* {{ $json.userName }}\n*Score:* {{ $json.opportunityScore }} {{ $json.leadTemperature }}\n\n*Latest Message:* _\"{{ $json.message }}\"_\n\n*Buying Signals:*\n{{ $json.detectedSignals.filter(s => !s.includes('❌') && !s.includes('💸') && !s.includes('⏸️') && !s.includes('🔍')).join('\\n') || 'None detected' }}\n\n*Next Steps:*\n{{ $json.recommendedActions.slice(0, 3).join('\\n') }}",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "name",
"value": "#sales-qualified"
},
"otherOptions": {
"mrkdwn": true
},
"authentication": "oAuth2"
},
"credentials": {
"slackOAuth2Api": {
"id": "fqQEQ6t5FN8BPrTs",
"name": "Slack account"
}
},
"typeVersion": 2.2
},
{
"id": "9efb26ba-abeb-48e3-ad95-e7a14c32ee34",
"name": "处理通话摘要",
"type": "n8n-nodes-base.code",
"position": [
720,
20
],
"parameters": {
"jsCode": "// Process end-of-call summary with final scoring\nconst callData = $input.first().json.body;\nconst messages = callData.messages || [];\n\n// Aggregate scoring across entire conversation\nlet totalScore = 0;\nconst allSignals = new Set();\nconst competitorMentions = new Set();\nlet maxDealValue = 0;\nlet userMessageCount = 0;\n\n// Process each message\nmessages.forEach(msg => {\n if (msg.sender === 'user') {\n userMessageCount++;\n const msgLower = msg.message.toLowerCase();\n \n // Check buying signals\n const buyingKeywords = [\n { keyword: 'interested', score: 15 },\n { keyword: 'budget', score: 15 },\n { keyword: 'timeline', score: 20 },\n { keyword: 'demo', score: 25 },\n { keyword: 'trial', score: 25 },\n { keyword: 'purchase', score: 30 },\n { keyword: 'implement', score: 30 },\n { keyword: 'decision maker', score: 25 },\n { keyword: 'our team', score: 20 }\n ];\n \n buyingKeywords.forEach(item => {\n if (msgLower.includes(item.keyword)) {\n totalScore += item.score;\n allSignals.add(`✅ ${item.keyword}`);\n }\n });\n \n // Check negative signals\n const negativeKeywords = [\n { keyword: 'not interested', score: -30 },\n { keyword: 'too expensive', score: -25 },\n { keyword: 'maybe later', score: -15 },\n { keyword: 'just looking', score: -10 }\n ];\n \n negativeKeywords.forEach(item => {\n if (msgLower.includes(item.keyword)) {\n totalScore += item.score;\n allSignals.add(`❌ ${item.keyword}`);\n }\n });\n \n // Check competitors\n const competitors = ['competitor', 'alternative', 'versus', 'comparing'];\n competitors.forEach(comp => {\n if (msgLower.includes(comp)) {\n competitorMentions.add(comp);\n }\n });\n }\n});\n\n// Calculate final metrics\nconst avgMessageScore = userMessageCount > 0 ? totalScore / userMessageCount : 0;\nconst qualificationLevel = \n totalScore >= 100 ? '🏆 Highly Qualified' :\n totalScore >= 50 ? '✅ Qualified' :\n totalScore >= 20 ? '🤔 Needs Nurturing' :\n '❄️ Not Qualified';\n\nreturn [{\n json: {\n callId: callData.call_id,\n userName: callData.call_data.userName,\n duration: callData.evaluation?.duration_minutes || 0,\n totalMessages: messages.length,\n userMessages: userMessageCount,\n finalScore: totalScore,\n averageScore: avgMessageScore.toFixed(1),\n qualificationLevel: qualificationLevel,\n uniqueSignals: Array.from(allSignals),\n competitorsMentioned: Array.from(competitorMentions),\n callSummary: {\n topic: callData.evaluation?.topic || 'General Discussion',\n sentiment: callData.evaluation?.user_sentiment || 'neutral',\n endTime: callData.call_data.endedAt\n },\n recommendations: generateFinalRecommendations(totalScore, allSignals),\n nextBestAction: totalScore >= 50 ? '📞 Call within 24 hours' : '📧 Add to nurture campaign'\n }\n}];\n\nfunction generateFinalRecommendations(score, signals) {\n const recs = [];\n if (score >= 100) {\n recs.push('🎯 Fast-track to sales manager');\n recs.push('📄 Prepare contract immediately');\n recs.push('💰 Custom pricing proposal needed');\n } else if (score >= 50) {\n recs.push('📧 Send personalized follow-up within 2 hours');\n recs.push('📅 Schedule next meeting this week');\n recs.push('📊 Prepare ROI calculation');\n } else {\n recs.push('🌱 Add to nurture campaign');\n recs.push('📚 Send educational content');\n recs.push('⏰ Check back in 30 days');\n }\n return recs;\n}"
},
"typeVersion": 2
},
{
"id": "86d700e6-027d-4771-b749-30ad2573bd34",
"name": "Slack：通话摘要",
"type": "n8n-nodes-base.slack",
"position": [
920,
20
],
"webhookId": "4efa4f35-14aa-4d1a-9d60-f7eff1f3c4f6",
"parameters": {
"text": "=📊 *Call Summary Report*\n\n*Lead:* {{ $json.userName }}\n*Call Duration:* {{ $json.duration }} minutes\n*Messages:* {{ $json.userMessages }} from user / {{ $json.totalMessages }} total\n\n*Final Qualification:* {{ $json.qualificationLevel }}\n*Total Score:* {{ $json.finalScore }} (Avg: {{ $json.averageScore }}/message)\n\n*Key Signals Detected:*\n{{ $json.uniqueSignals.join('\\n') || 'No strong signals detected' }}\n\n*Competitors Mentioned:* {{ $json.competitorsMentioned.length > 0 ? $json.competitorsMentioned.join(', ') : 'None' }}\n\n*Next Best Action:* {{ $json.nextBestAction }}\n\n*Recommendations:*\n{{ $json.recommendations.join('\\n') }}\n\n---\n_Call ID: `{{ $json.callId }}` | Ended: {{ $json.callSummary.endTime }}_",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "name",
"value": "#sales-summaries"
},
"otherOptions": {
"mrkdwn": true
},
"authentication": "oAuth2"
},
"credentials": {
"slackOAuth2Api": {
"id": "fqQEQ6t5FN8BPrTs",
"name": "Slack account"
}
},
"typeVersion": 2.2
},
{
"id": "786595a2-20fc-4948-89da-918fc08d6b1d",
"name": "设置说明",
"type": "n8n-nodes-base.stickyNote",
"position": [
1500,
-500
],
"parameters": {
"width": 340,
"height": 540,
"content": "## 🔧 配置步骤"
},
"typeVersion": 1
}
],
"active": true,
"pinData": {
"BeyondPresence Real-Time Webhook": [
{
"json": {
"body": {
"call_id": "BZ0MTsPQUOJngw4k5Txg",
"message": {
"sender": "user",
"message": "I want to know about weather",
"sent_at": "2025-05-28T10:26:32.104000+00:00"
},
"call_data": {
"agentId": "1c540d63-8044-4e87-9fc1-e713abb60944",
"userName": "Shehroz Sajjad"
},
"event_type": "message"
},
"query": {},
"params": {},
"headers": {
"host": "bey-test.app.n8n.cloud",
"accept": "*/*",
"cf-ray": "946d0def44f1bb74-FRA",
"cdn-loop": "cloudflare; loops=1; subreqs=1",
"cf-ew-via": "15",
"cf-worker": "n8n.cloud",
"x-real-ip": "2600:1900:0:3401::e00",
"cf-visitor": "{\"scheme\":\"https\"}",
"user-agent": "python-requests/2.31.0",
"cf-ipcountry": "US",
"content-type": "application/json",
"x-is-trusted": "yes",
"content-length": "277",
"accept-encoding": "gzip, br",
"x-forwarded-for": "2600:1900:0:3401::e00, 172.70.246.133",
"cf-connecting-ip": "2600:1900:0:3401::e00",
"x-forwarded-host": "bey-test.app.n8n.cloud",
"x-forwarded-port": "443",
"x-forwarded-proto": "https",
"x-forwarded-server": "traefik-prod-users-gwc-72-585f59b6c7-zdqd9"
},
"webhookUrl": "https://bey-test.app.n8n.cloud/webhook/beyondpresence-sales-intelligence",
"executionMode": "production"
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "a83151b3-1111-41e2-a9f9-b8680f2dd64f",
"connections": {
"Route Alerts": {
"main": [
[
{
"node": "Slack: Hot Lead Alert",
"type": "main",
"index": 0
}
],
[
{
"node": "Slack: Competitor Alert",
"type": "main",
"index": 0
}
],
[
{
"node": "Slack: Qualified Lead",
"type": "main",
"index": 0
}
]
]
},
"Acknowledge Receipt": {
"main": [
[
{
"node": "Filter User Messages",
"type": "main",
"index": 0
}
]
]
},
"Filter User Messages": {
"main": [
[
{
"node": "Score Sales Opportunity",
"type": "main",
"index": 0
}
],
[]
]
},
"Process Call Summary": {
"main": [
[
{
"node": "Slack: Call Summary",
"type": "main",
"index": 0
}
]
]
},
"Score Sales Opportunity": {
"main": [
[
{
"node": "Route Alerts",
"type": "main",
"index": 0
}
]
]
},
"BeyondPresence Real-Time Webhook": {
"main": [
[
{
"node": "Acknowledge Receipt",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
中级 - 销售, 人工智能
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
中级
节点数量14
分类2
节点类型6
作者
M Shehroz Sajjad
@mshehrozsajjad🚀 BeyondPresence Integration Specialist | n8n Expert Building the future of conversational AI automation. Creator of the BeyondPresence template collection for n8n, enabling real-time conversation intelligence, automated insights, and seamless business system integration. Specialties: Video agent automation, real-time webhooks, AI analysis, conversation intelligence
外部链接
在 n8n.io 查看 →
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