{ "id": "UOscJQ4gPnLcc904", "meta": { "templateCredsSetupCompleted": true }, "name": "AI Based Deal Timeline Predictor", "tags": [], "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": { "name": "" } }, "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 ], "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": { "name": "" } }, "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": { "name": "" } }, "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": { "name": "" } }, "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": { "name": "" } }, "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": { "name": "" } }, "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": { "name": "" } }, "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": { "name": "" } }, "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 }, { 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