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Close email verification with BillionVerify
Close is a sales CRM built for high-velocity outreach, combining email, calling, and pipeline management in one place. BillionVerify integrates with Close to validate prospect emails before sequences start, protecting your sender reputation and ensuring every outreach attempt lands in a real inbox.
Why verify before the send
Sales teams rely on accurate contact data to hit their numbers, but lists sourced from imports, enrichment tools, or web forms inevitably contain bad addresses. In Close, a single unverified import can trigger a wave of bounces that damages your domain reputation for weeks. BillionVerify screens out invalid, disposable, role-based, and catch-all addresses before they ever enter an active sequence.
Ready-to-use n8n workflow
Import this workflow into n8n — it verifies every address with BillionVerify before Close sends, so only deliverable contacts are emailed. Install the BillionVerify community node first, then add your API key. Adapted from this n8n template
verify-emails-in-close.json
{
"name": "Predict deal close dates and update Salesforce with Groq and Google Sheets + BillionVerify",
"nodes": [
{
"id": "2d2c59b6-1d23-4124-95c3-caba52218f0b",
"name": "AI Deal Timeline Predictor",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
16,
-960
],
"parameters": {
"text": "=Analyze this opportunity and predict the most realistic close timeline.\n\nOpportunity Details:\nName: {{ $json.Name }}\nStage: {{ $json.StageName }}\nAmount: {{ $json.Amount }}\nCurrent Probability: {{ $json.Probability }}\nCurrent Close Date: {{ $json.CloseDate }}\nLead Source: {{ $json.LeadSource }}\n\nHistorical Intelligence:\nTotal Historical Deals: {{ $json.totalHistoricalDeals }}\nWon Historical Deals: {{ $json.wonHistoricalDeals }}\nHistorical Win Rate: {{ $json.historicalWinRate }}\nAverage Historical Amount: {{ $json.avgHistoricalAmount }}\nAmount Deviation: {{ $json.amountDeviation }}\n\nFeature Engineered Signals:\nDays Open: {{ $json.daysOpen }}\nDeal Velocity: {{ $json.dealVelocity }}\nActivity Score: {{ $json.activityScore }}\nEngagement Level: {{ $json.engagementLevel }}\nStage Weight: {{ $json.stageWeight }}\nTime Pressure (days remaining): {{ $json.timePressure }}\n\nRisk Assessment:\nRisk Score: {{ $json.riskScore }}\nRisk Label: {{ $json.riskLabel }}\n\nInstructions:\n- Prioritize engagement level and activity score to assess deal momentum\n- Use historical win rate and amount deviation to benchmark deal quality\n- Use days open and time pressure to adjust realism of timeline\n- Use stage and probability to estimate conversion likelihood\n\nGenerate the prediction in valid JSON only.",
"options": {
"systemMessage": "You are an enterprise-grade sales intelligence AI specializing in B2B opportunity forecasting.\n\nYour role is to predict realistic deal closure timelines and win probabilities using a combination of:\n- current opportunity stage and probability\n- historical performance benchmarks\n- feature-engineered signals (engagement, velocity, time pressure)\n- calculated risk score\n\nDecision Guidelines:\n- High engagement and activity increase win probability\n- Low activity or low engagement significantly reduces confidence\n- Large negative amount deviation indicates higher risk\n- Older deals (high daysOpen) tend to have lower success probability\n- High time pressure (few days left) requires realistic or extended timelines\n- Strong historical win rate should positively influence prediction\n\nAlways respond ONLY in valid JSON.\n\nDo not include markdown, explanations or extra text.\n\nUse this exact response schema:\n\n{\n \"predicted_close_date\": \"YYYY-MM-DD\",\n \"predicted_win_probability\": number,\n \"confidence_score\": number,\n \"reasoning\": \"short explanation\",\n \"next_best_action\": \"recommended sales action\"\n}\n\nRules:\n- confidence_score must be between 0 and 100\n- predicted_win_probability must be between 0 and 100\n- predicted_close_date must reflect realistic deal progression\n- reasoning must clearly reference risk, engagement and historical trends\n- next_best_action must be actionable and aligned with deal stage"
},
"promptType": "define"
},
"executeOnce": false,
"typeVersion": 3.1
},
{
"id": "cde3525a-1560-4ed9-ba32-12617ae2acc2",
"name": "Parse AI Output",
"type": "n8n-nodes-base.code",
"position": [
368,
-960
],
"parameters": {
"jsCode": "let rawOutput = $input.first().json.output;\n// Strip markdown formatting if the LLM adds it\nrawOutput = rawOutput.replace(/```json\\n?/g, '').replace(/```/g, '').trim();\n\nlet parsed;\ntry {\n parsed = JSON.parse(rawOutput);\n} catch (error) {\n throw new Error(`Invalid JSON returned from AI: ${rawOutput}`);\n}\n\nreturn [{ json: parsed }];"
},
"typeVersion": 2
},
{
"id": "161857c4-0c4a-4334-999a-aac22fd0b5d6",
"name": "Process Opportunities",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-1040,
-672
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "168d4921-36c5-4018-a36d-b0d0e035faa0",
"name": "Insights",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
16,
-752
],
"parameters": {
"model": "llama-3.3-70b-versatile",
"options": {}
},
"credentials": {
"groqApi": {
"id": "ZYz2lsdEbgg9Owa1",
"name": "Groq account 2"
}
},
"typeVersion": 1
},
{
"id": "cb63203a-1655-4d54-a1ab-343961255177",
"name": "check confidence score",
"type": "n8n-nodes-base.if",
"position": [
592,
-960
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 3,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "5bd2c425-fc48-4235-965d-ae61248f9350",
"operator": {
"type": "number",
"operation": "gte"
},
"leftValue": "={{ $json.confidence_score }}",
"rightValue": 70
}
]
}
},
"typeVersion": 2.3
},
{
"id": "8c09cb03-c5ac-4339-91dc-523124946294",
"name": "alert for Manual review",
"type": "n8n-nodes-base.gmail",
"position": [
816,
-864
],
"webhookId": "a397efc9-f93b-41bb-ba77-f1b4bf53d62c",
"parameters": {
"message": "=AI prediction confidence below threshold.\n\nOpportunity requires sales manager review.\n\nPredicted Close Date: {{ $json.predicted_close_date }}\nConfidence: {{ $json.confidence_score }}\nRecommended Action: {{ $json.next_best_action }}",
"options": {
"appendAttribution": false
},
"subject": "Manual review alert",
"emailType": "text"
},
"credentials": {
"gmailOAuth2": {
"id": "fAXxeaXx4aurF9lx",
"name": "Gmail account"
}
},
"typeVersion": 2.2
},
{
"id": "ea977f2f-3b00-4fb1-bdcc-b704aa05a00b",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2816,
-1200
],
"parameters": {
"width": 832,
"height": 784,
"content": "\n# AI-Based Deal Timeline Predictor\n\nThis workflow provides an **end-to-end automation pipeline** that continuously evaluates **active Salesforce opportunities** to optimize your sales forecasting. It intelligently engineers **custom momentum features** and leverages a powerful **LLM agent** to accurately predict realistic **close dates** and **win probabilities**. By dynamically routing actions based on **AI confidence scores**, it ensures that routine updates are handled instantly while flagging high-risk deals for **human-in-the-loop review**.\n\n\n## How it Works:\n1. Runs on a rolling schedule, fetching recently modified open deals alongside historical closed/won data for benchmarking.\n2. Iterates through each deal using a rate-controlled batching queue.\n3. Pulls granular task/activity data for each specific deal to assess engagement.\n4. Calculates an intelligent **Risk Score** and **Velocity** metric using custom JS before sending the payload to a Groq Llama-3 LangChain agent.\n5. Evaluates the AI's **Confidence Score**:\n - **High Confidence:** Auto-updates Salesforce Close Date.\n - **Low Confidence:** Alerts management via Gmail for manual review.\n - *Both paths log comprehensive audit trails to Google Sheets.*\n\n## Setup Steps:\n* **Salesforce Configuration:** Authenticate your account to enable querying of open opportunities, historical deals and related task activities.\n* **LLM Integration:** Provide your Groq API key to power the Llama-3 LangChain intelligence engine.\n* **Email Alerts:** Connect your Gmail account to properly route low-confidence predictions to management.\n* **Audit Logging:** Link a target Google Sheet to maintain a comprehensive record of all automated updates and manual review requests.\n* **Trigger Scheduling:** Set your desired interval on the schedule trigger to dictate how often the batch process runs.\n\n"
},
"typeVersion": 1
},
{
"id": "9dbb5905-0903-48bc-9ce7-dbcbb9c140e2",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1840,
-1056
],
"parameters": {
"color": 7,
"width": 688,
"height": 752,
"content": "### Data Ingestion & Initialization\n\nThis phase triggers the workflow on a set schedule and establishes the lookback time window for recent updates. It queries Salesforce to simultaneously retrieve recently modified active opportunities and a static batch of historical closed-won deals. Fetching this data upfront ensures the AI has both the current deal context and the historical baselines required for accurate forecasting before entering the processing loop."
},
"typeVersion": 1
},
{
"id": "6cc0b5e4-d15d-499f-aad1-34a57b68a9ff",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1120,
-1152
],
"parameters": {
"color": 7,
"width": 848,
"height": 816,
"content": "### Opportunity Enrichment & Validation\n\nThis section isolates each deal using a **rate-controlled batching queue** to prevent API limit exhaustion. For each deal, it fetches granular **Salesforce task activities** to accurately measure recent engagement. Finally, it uses an IF node to **filter out invalid or closed deals**, ensuring that only active, high-quality opportunities are allowed to consume LLM tokens."
},
"typeVersion": 1
},
{
"id": "78d13f8e-5f9b-4cc5-b501-281196ec20e7",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-256,
-1136
],
"parameters": {
"color": 7,
"width": 768,
"height": 512,
"content": "### AI Intelligence & Feature Engineering\n\nFirst, a custom JavaScript node calculates **Deal Velocity**, **Days Open** and a dynamic **Risk Score** based on stage stagnation. These engineered signals are then processed by a **Groq-powered LangChain Agent**, which acts as a sales strategist to predict the **close date and win probability**. The raw LLM response is strictly parsed into a structured **JSON object** for reliable downstream routing."
},
"typeVersion": 1
},
{
"id": "a4c36447-7dbe-4e76-9d99-e2a70043414c",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
544,
-1264
],
"parameters": {
"color": 7,
"width": 672,
"height": 960,
"content": "## 4. Conditional Execution & Logging\n\nThis routing logic dictates action based on the AI's predicted **Confidence Score** using a strict threshold of 70. High-confidence predictions automatically **update the close date in Salesforce**, while low-confidence predictions trigger a **Gmail alert for manual manager review**. Both distinct outcomes maintain a comprehensive audit trail by logging success or pending statuses to **Google Sheets**."
},
"typeVersion": 1
},
{
"id": "8eeba1bb-780b-4de4-8b22-a3f15810e617",
"name": "Run Schedule",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
-1792,
-768
],
"parameters": {
"rule": {
"interval": [
{}
]
}
},
"typeVersion": 1.3
},
{
"id": "80443cf9-00d8-4fbf-a4ea-539dfa722d7a",
"name": "Set Lookback Timeframe",
"type": "n8n-nodes-base.set",
"position": [
-1616,
-768
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ef71ac17-4837-4a63-ad26-f41fa320670d",
"name": "lastruntime",
"type": "string",
"value": "={{new Date(Date.now() - 5 * 60 * 1000).toISOString()}}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "d56ac1b6-ed5d-4a00-853e-28372ed14158",
"name": "Fetch Historical Won Deals",
"type": "n8n-nodes-base.salesforce",
"position": [
-1376,
-864
],
"parameters": {
"query": "SELECT Id, Name, StageName, Amount, Probability, CloseDate, IsWon, IsClosed, LeadSource, CreatedDate\nFROM Opportunity\nWHERE IsClosed = true\nORDER BY CloseDate DESC\nLIMIT 10",
"resource": "search"
},
"credentials": {
"salesforceOAuth2Api": {
"id": "4cOnMMVTeqyQiqUU",
"name": "Salesforce account"
}
},
"executeOnce": true,
"typeVersion": 1
},
{
"id": "3d134bf8-b1fb-4209-ac36-a1f143b0b95f",
"name": "Fetch Recent Active Deals",
"type": "n8n-nodes-base.salesforce",
"position": [
-1376,
-672
],
"parameters": {
"query": "=SELECT Id, Name, StageName, Amount, Probability, CloseDate, LastModifiedDate\nFROM Opportunity\nWHERE \n LastModifiedDate > {{ $json.lastruntime }}\n AND IsClosed = false\n AND Amount > 0\nORDER BY LastModifiedDate DESC\nLIMIT 20",
"resource": "search"
},
"credentials": {
"salesforceOAuth2Api": {
"id": "4cOnMMVTeqyQiqUU",
"name": "Salesforce account"
}
},
"typeVersion": 1
},
{
"id": "34b3a0f8-0fd6-407a-8ad5-c13ebd14b01d",
"name": "Fetch Deal Details",
"type": "n8n-nodes-base.salesforce",
"position": [
-896,
-960
],
"parameters": {
"resource": "opportunity",
"operation": "get",
"opportunityId": "={{ $json.Id }}"
},
"credentials": {
"salesforceOAuth2Api": {
"id": "4cOnMMVTeqyQiqUU",
"name": "Salesforce account"
}
},
"typeVersion": 1
},
{
"id": "cdb0595f-a3b3-490e-98ea-1bc1ccd743d3",
"name": "Deal Task History",
"type": "n8n-nodes-base.salesforce",
"position": [
-688,
-960
],
"parameters": {
"query": "=SELECT ActivityDate, Subject, Status\nFROM Task\nWHERE WhatId = '{{ $json.Id }}'\nORDER BY ActivityDate DESC\nLIMIT 20",
"resource": "search"
},
"credentials": {
"salesforceOAuth2Api": {
"id": "4cOnMMVTeqyQiqUU",
"name": "Salesforce account"
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "9e4e51d7-6779-4e3c-ad75-dba23181c511",
"name": "Is Deal Valid & Active?",
"type": "n8n-nodes-base.if",
"position": [
-480,
-960
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 3,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "a92fed93-bea0-47cc-9b85-fd46b46887e3",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $('Fetch Deal Details').item.json.Amount }}",
"rightValue": 0
},
{
"id": "dc750fbc-f2b2-4a8e-b7c3-3c1a3371ad6b",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Fetch Deal Details').item.json.IsClosed }}",
"rightValue": "false"
},
{
"id": "35e30051-70b9-4744-8b7a-532f08b06359",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $('Fetch Deal Details').item.json.Probability }}",
"rightValue": 0
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.3
},
{
"id": "643493d0-5f35-47f9-aba3-33e02ebc897d",
"name": "Calculate Deal Risk & Velocity",
"type": "n8n-nodes-base.code",
"position": [
-208,
-960
],
"parameters": {
"jsCode": "// Current deal\nconst currentDeal = $(\"Fetch Deal Details\").first().json;\n\n// Historical deals\nconst historicalDeals = $(\"Fetch Historical Won Deals\").all().map(item => item.json);\n\n// Activities\nconst activities = $(\"Deal Task History\").all().map(item => item.json);\n\n// --------------------\n// Feature Engineering\n// --------------------\n\n// Days open \nconst createdDate = new Date(currentDeal.CreatedDate);\nconst today = new Date();\n\nconst daysOpen = Math.max(\n 1,\n Math.ceil((today - createdDate) / (1000 * 60 * 60 * 24))\n);\n\n// Deal velocity\nconst dealVelocity = (currentDeal.Amount || 0) / daysOpen;\n\n// Activity score\nconst activityScore = activities.length;\n\n// Engagement level\nlet engagementLevel = \"Low\";\nif (activityScore > 10) engagementLevel = \"High\";\nelse if (activityScore > 5) engagementLevel = \"Medium\";\n\n// Stage weight (ML-style encoding)\nconst stageWeights = {\n \"Prospecting\": 1,\n \"Qualification\": 2,\n \"Proposal/Price Quote\": 3,\n \"Negotiation/Review\": 4,\n \"Closed Won\": 5\n};\n\nconst stageWeight = stageWeights[currentDeal.StageName] || 1;\n\n// Time pressure (days left)\nlet timePressure = 0;\nif (currentDeal.CloseDate) {\n const closeDate = new Date(currentDeal.CloseDate);\n timePressure = Math.floor((closeDate - today) / (1000 * 60 * 60 * 24));\n}\n\n// --------------------\n// Historical calculations\n// --------------------\nconst totalDeals = historicalDeals.length;\n\nconst wonDeals = historicalDeals.filter(d => d.IsWon === true).length;\n\nconst totalAmount = historicalDeals.reduce((sum, d) => {\n return sum + (d.Amount || 0);\n}, 0);\n\nconst avgHistoricalAmount =\n totalDeals > 0 ? totalAmount / totalDeals : 0;\n\nconst historicalWinRate =\n totalDeals > 0 ? (wonDeals / totalDeals) * 100 : 0;\n\nconst amountDeviation =\n (currentDeal.Amount || 0) - avgHistoricalAmount;\n\n// --------------------\n// Risk scoring (IMPROVED)\n// --------------------\nlet riskScore = 0;\n\n// Probability-based risk\nif ((currentDeal.Probability || 0) < 50) riskScore += 30;\nelse if ((currentDeal.Probability || 0) < 70) riskScore += 15;\n\n// Amount deviation risk\nif (amountDeviation < -50000) riskScore += 20;\n\n// Stage risk\nif (currentDeal.StageName === \"Prospecting\") riskScore += 25;\nif (currentDeal.StageName === \"Qualification\") riskScore += 15;\nif (currentDeal.StageName === \"Negotiation/Review\") riskScore += 10;\n\n// Historical win-rate risk\nif (historicalWinRate < 70) riskScore += 20;\n\n// Activity-based risk \nif (activityScore === 0) riskScore += 30;\nelse if (activityScore < 3) riskScore += 20;\nelse if (activityScore < 6) riskScore += 10;\n\n// Time pressure risk\nif (timePressure < 5) riskScore += 10;\n\n// Deal aging risk (\nif (daysOpen > 30) riskScore += 10;\n\n// Cap at 100\nriskScore = Math.min(riskScore, 100);\n\n// Risk label\nlet riskLabel = \"Low\";\nif (riskScore >= 70) riskLabel = \"High\";\nelse if (riskScore >= 40) riskLabel = \"Medium\";\n\n// --------------------\n// Final output\n// --------------------\nreturn [\n {\n json: {\n ...currentDeal,\n\n // Historical\n totalHistoricalDeals: totalDeals,\n wonHistoricalDeals: wonDeals,\n avgHistoricalAmount,\n historicalWinRate,\n amountDeviation,\n\n // Feature Engineering\n daysOpen,\n dealVelocity,\n activityScore,\n engagementLevel,\n stageWeight,\n timePressure,\n\n // Risk\n riskScore,\n riskLabel\n }\n }\n];"
},
"executeOnce": true,
"typeVersion": 2
},
{
"id": "72d6c973-506c-49e7-83ab-96d3cc678704",
"name": "Update Close Date",
"type": "n8n-nodes-base.salesforce",
"position": [
816,
-1056
],
"parameters": {
"resource": "opportunity",
"operation": "update",
"updateFields": {
"closeDate": "={{ $json.predicted_close_date }}"
},
"opportunityId": "={{ $('Fetch Deal Details').item.json.Id }}"
},
"credentials": {
"salesforceOAuth2Api": {
"id": "4cOnMMVTeqyQiqUU",
"name": "Salesforce account"
}
},
"typeVersion": 1
},
{
"id": "d1a3ac62-9dd6-4053-96b6-60ca176e2048",
"name": "Log Auto-Update Success",
"type": "n8n-nodes-base.googleSheets",
"position": [
1040,
-720
],
"parameters": {
"columns": {
"value": {
"status": "SUCCESS",
"reasoning": "={{ $(\"Parse AI Output\").first().json.reasoning }}",
"timestamp": "={{ $now }}",
"risk_label": "={{ $(\"Calculate Deal Risk & Velocity\").first().json.riskLabel }}",
"risk_score": "={{ $(\"Calculate Deal Risk & Velocity\").first().json.riskScore }}",
"stage_name": "={{ $(\"Fetch Deal Details\").first().json.StageName }}",
"action_taken": "AUTO_UPDATED",
"current_amount": "={{ $(\"Fetch Deal Details\").first().json.Amount }}",
"opportunity_id": "={{ $(\"Fetch Deal Details\").first().json.Id }}",
"confidence_score": "={{ $(\"Parse AI Output\").first().json.confidence_score }}",
"next_best_action": "={{ $(\"Parse AI Output\").first().json.next_best_action }}",
"opportunity_name": "={{ $(\"Fetch Deal Details\").first().json.Name }}",
"predicted_close_date": "={{ $(\"Parse AI Output\").first().json.predicted_close_date }}",
"predicted_win_probability": "={{ $(\"Parse AI Output\").first().json.predicted_win_probability }}"
},
"schema": [
{
"id": "timestamp",
"type": "string",
"display": true,
"required": false,
"displayName": "timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "opportunity_id",
"type": "string",
"display": true,
"required": false,
"displayName": "opportunity_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "opportunity_name",
"type": "string",
"display": true,
"required": false,
"displayName": "opportunity_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "stage_name",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "stage_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "current_amount",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "current_amount",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "risk_score",
"type": "string",
"display": true,
"required": false,
"displayName": "risk_score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "risk_label",
"type": "string",
"display": true,
"required": false,
"displayName": "risk_label",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "predicted_close_date",
"type": "string",
"display": true,
"required": false,
"displayName": "predicted_close_date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "predicted_win_probability",
"type": "string",
"display": true,
"required": false,
"displayName": "predicted_win_probability",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "confidence_score",
"type": "string",
"display": true,
"required": false,
"displayName": "confidence_score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "action_taken",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "action_taken",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "status",
"type": "string",
"display": true,
"required": false,
"displayName": "status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "reasoning",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "reasoning",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "next_best_action",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "next_best_action",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c/edit?usp=drivesdk",
"cachedResultName": "update prediction crm"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "m0H33sXFyEfCABEN",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"id": "0af294ed-0993-4baf-9b74-afa12d141c85",
"name": "Log Pending Review",
"type": "n8n-nodes-base.googleSheets",
"position": [
1040,
-544
],
"parameters": {
"columns": {
"value": {
"status": "PENDING_MANAGER_REVIEW",
"reasoning": "={{ $(\"Parse AI Output\").first().json.reasoning }}",
"timestamp": "={{ $now }}",
"risk_label": "={{ $(\"Calculate Deal Risk & Velocity\").first().json.riskLabel }}",
"risk_score": "={{ $(\"Calculate Deal Risk & Velocity\").first().json.riskScore }}",
"stage_name": "={{ $(\"Fetch Deal Details\").first().json.StageName }}",
"action_taken": "MANUAL_REVIEW",
"current_amount": "={{ $(\"Fetch Deal Details\").first().json.Amount }}",
"opportunity_id": "={{ $(\"Fetch Deal Details\").first().json.Id }}",
"confidence_score": "={{ $(\"Parse AI Output\").first().json.confidence_score }}",
"next_best_action": "={{ $(\"Parse AI Output\").first().json.next_best_action }}",
"opportunity_name": "={{ $(\"Fetch Deal Details\").first().json.Name }}",
"predicted_close_date": "={{ $(\"Parse AI Output\").first().json.predicted_close_date }}",
"predicted_win_probability": "={{ $(\"Parse AI Output\").first().json.predicted_win_probability }}"
},
"schema": [
{
"id": "timestamp",
"type": "string",
"display": true,
"required": false,
"displayName": "timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "opportunity_id",
"type": "string",
"display": true,
"required": false,
"displayName": "opportunity_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "opportunity_name",
"type": "string",
"display": true,
"required": false,
"displayName": "opportunity_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "stage_name",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "stage_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "current_amount",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "current_amount",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "risk_score",
"type": "string",
"display": true,
"required": false,
"displayName": "risk_score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "risk_label",
"type": "string",
"display": true,
"required": false,
"displayName": "risk_label",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "predicted_close_date",
"type": "string",
"display": true,
"required": false,
"displayName": "predicted_close_date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "predicted_win_probability",
"type": "string",
"display": true,
"required": false,
"displayName": "predicted_win_probability",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "confidence_score",
"type": "string",
"display": true,
"required": false,
"displayName": "confidence_score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "action_taken",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "action_taken",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "status",
"type": "string",
"display": true,
"required": false,
"displayName": "status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "reasoning",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "reasoning",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "next_best_action",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "next_best_action",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1xZ3AqllVeBEXx9N2LF6Sp6l2iIUS6-2TwQ2MINPE44c/edit?usp=drivesdk",
"cachedResultName": "update prediction crm"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "m0H33sXFyEfCABEN",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"parameters": {
"operation": "verify",
"email": "={{ $json.email || $json.Email }}",
"additionalOptions": {}
},
"type": "n8n-nodes-billionverify.billionVerify",
"typeVersion": 1,
"position": [
456,
-864
],
"name": "Verify Email (BillionVerify)",
"credentials": {
"billionVerifyApi": {
"id": "",
"name": "BillionVerify account"
}
}
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "is-deliverable",
"leftValue": "={{ $json.is_deliverable }}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
}
}
]
}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2,
"position": [
636,
-864
],
"name": "IF deliverable"
}
],
"connections": {
"Insights": {
"ai_languageModel": [
[
{
"node": "AI Deal Timeline Predictor",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Run Schedule": {
"main": [
[
{
"node": "Set Lookback Timeframe",
"type": "main",
"index": 0
}
]
]
},
"Parse AI Output": {
"main": [
[
{
"node": "check confidence score",
"type": "main",
"index": 0
}
]
]
},
"Deal Task History": {
"main": [
[
{
"node": "Is Deal Valid & Active?",
"type": "main",
"index": 0
}
]
]
},
"Update Close Date": {
"main": [
[
{
"node": "Log Auto-Update Success",
"type": "main",
"index": 0
}
]
]
},
"Fetch Deal Details": {
"main": [
[
{
"node": "Deal Task History",
"type": "main",
"index": 0
}
]
]
},
"Log Pending Review": {
"main": [
[
{
"node": "Process Opportunities",
"type": "main",
"index": 0
}
]
]
},
"Process Opportunities": {
"main": [
[],
[
{
"node": "Fetch Deal Details",
"type": "main",
"index": 0
}
]
]
},
"Set Lookback Timeframe": {
"main": [
[
{
"node": "Fetch Historical Won Deals",
"type": "main",
"index": 0
},
{
"node": "Fetch Recent Active Deals",
"type": "main",
"index": 0
}
]
]
},
"check confidence score": {
"main": [
[
{
"node": "Update Close Date",
"type": "main",
"index": 0
}
],
[
{
"node": "Verify Email (BillionVerify)",
"type": "main",
"index": 0
}
]
]
},
"Is Deal Valid & Active?": {
"main": [
[
{
"node": "Calculate Deal Risk & Velocity",
"type": "main",
"index": 0
}
]
]
},
"Log Auto-Update Success": {
"main": [
[
{
"node": "Process Opportunities",
"type": "main",
"index": 0
}
]
]
},
"alert for Manual review": {
"main": [
[
{
"node": "Log Pending Review",
"type": "main",
"index": 0
}
]
]
},
"Fetch Recent Active Deals": {
"main": [
[
{
"node": "Process Opportunities",
"type": "main",
"index": 0
}
]
]
},
"AI Deal Timeline Predictor": {
"main": [
[
{
"node": "Parse AI Output",
"type": "main",
"index": 0
}
]
]
},
"Fetch Historical Won Deals": {
"main": [
[]
]
},
"Calculate Deal Risk & Velocity": {
"main": [
[
{
"node": "AI Deal Timeline Predictor",
"type": "main",
"index": 0
}
]
]
},
"Verify Email (BillionVerify)": {
"main": [
[
{
"node": "IF deliverable",
"type": "main",
"index": 0
}
]
]
},
"IF deliverable": {
"main": [
[
{
"node": "alert for Manual review",
"type": "main",
"index": 0
}
],
[]
]
}
},
"settings": {
"binaryMode": "separate",
"executionOrder": "v1"
}
}Workflow templates with Close
Ready-to-use workflows that verify emails before Close sends.
How it works
- 1
Connect Close to n8n using the Close node, or set up a trigger via Integrately.
- 2
On lead creation or import, pass the prospect email to BillionVerify's API.
- 3
BillionVerify returns a verification result covering deliverability, disposable status, catch-all flag, and role address detection.
- 4
Use workflow logic to add a tag or custom field in Close reflecting the verification outcome.
- 5
Filter active sequences to include only leads tagged as verified-valid, keeping bounce rates well below email provider thresholds.
When to use this
Screen leads before adding to sequences
When a new lead is created in Close, trigger a BillionVerify check before enrolling them in an email sequence. Only deliverable addresses enter your cadences, reducing bounce rates and keeping your domain reputation strong.
Validate bulk lead imports
Before importing a CSV of prospects into Close, run the list through BillionVerify's bulk API. Remove bad addresses upstream so your sales team spends time on real opportunities, not bounced emails.
Flag catch-all and role addresses for human review
BillionVerify identifies catch-all domains and role-based emails like sales@ or info@ that may never reach a decision-maker. Route these to a review queue in Close rather than automating outreach to them.
FAQ
Why does bounce rate matter for a CRM like Close?
Close sends email directly through your connected mailbox. High bounce rates signal to email providers that your domain sends to bad addresses, which can trigger spam filtering or even domain blacklisting, hurting deliverability across all outreach.
Can BillionVerify detect addresses from free providers like Gmail or Yahoo?
Yes. BillionVerify identifies the provider type for each address, including free webmail providers, which helps sales teams decide how to prioritize or personalize outreach.
How do I set up the integration without developer resources?
Use Integrately's 1-click BillionVerify integration to connect Close triggers to verification in minutes — no coding required. For custom logic, the n8n community node offers more advanced workflow control.
Should I verify existing contacts already in Close?
Yes, especially for contacts that have been inactive or were imported months ago. Email deliverability degrades over time as people change jobs. Running periodic bulk verification on your Close contacts keeps the database current.
Verify emails in Close
Create a free account, grab your API key, and stop bounces before they happen.
Get started free