{ "workflow": { "id": 9309, "name": "Employee attrition risk detection & HR alerts using Azure OpenAI GPT-4o-mini & Gmail", "views": 146, "recentViews": 0, "totalViews": 146, "createdAt": "2025-10-06T13:20:25.086Z", "description": "## Description \nAutomatically ingests new employee data, extracts relevant signals, scores attrition risk, and notifies HR/managers with structured insights and recommended actions. Built on Azure OpenAI Chat with Structured Output Parser and true/false routing for escalation.\n\n## What This Template Does \n- Trigger for new data: Starts when a new profile, survey, or report file is added.\n- Download & extract: Retrieves the file and converts PDFs/text into analyzable content.\n- Analyze signals: Uses Azure OpenAI Chat to interpret sentiment, workload, performance notes, feedback, and changes (role, compensation, manager, location).\n- Structured parsing: Maps to fields like risk_score, risk_level, key_drivers, recommended_interventions, escalation_required.\n- Logic routing: Applies thresholds (e.g., risk_score ≥ 0.7) and flags for urgent follow-up.\n- Email alerts: Drafts and sends tailored notifications to HR/manager with action steps.\n\n## Key Signals Considered \n- Sentiment & language: Negative tone, burnout cues, disengagement in feedback.\n- Activity trends: Drop in participation, delayed responses, meeting absenteeism.\n- Performance & goals: Recent rating changes, missed OKRs, quality issues.\n- Role & compensation: Lateral moves, pay gaps vs. market, stalled progression.\n- Managerial context: Team churn, conflict mentions, low recognition frequency.\n\n## Features \n- Azure OpenAI Chat: Interprets unstructured text into consistent risk fields.\n- Structured Output Parser: Guarantees schema for downstream decisions.\n- Conditional Logic (true/false): Threshold checks for escalation.\n- Memory: Maintains context across multiple files per employee for trend-aware scoring.\n- Calculate avg span: Computes tenure or recency metrics used in risk scoring.\n- Email Composer & Sender: Generates and dispatches HR-ready alerts.\n\n## Requirements \n- n8n instance with access to employee data sources (Drive, Inbox, HR folder).\n- Extract From PDF configured for clean text output.\n- Azure OpenAI credentials (e.g., GPT‑4o‑mini) connected to Chat Model.\n- Email service (Gmail/SMTP) set in n8n Credentials.\n- Parser schema aligned to your People Analytics fields (risk_score, drivers, actions).", "workflow": { "id": "ONqZuWnwkE64Mm4F", "meta": { "instanceId": "8443f10082278c46aa5cf3acf8ff0f70061a2c58bce76efac814b16290845177", "templateCredsSetupCompleted": true }, "name": "Attrition Risk Alert Workflow (Azure OpenAI + n8n)", "tags": [], "nodes": [ { "id": "c48bef67-a0d0-4a4b-a374-3df1a7ed4c19", "name": "Azure OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi", "position": [ 688, 128 ], "parameters": { "model": "gpt-4o-mini", "options": {} }, "credentials": { "azureOpenAiApi": { "id": "credential-id", "name": "azureOpenAiApi Credential" } }, "typeVersion": 1 }, { "id": "a44062d6-1e37-443b-b146-cf32a9ac74cc", "name": "Structured Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ 816, 128 ], "parameters": { "jsonSchemaExample": "{\n\t\"average\": \"18\"\n}" }, "typeVersion": 1.3 }, { "id": "2ab2bd76-1809-4015-9d99-a278561287da", "name": "Trigger for new resume", "type": "n8n-nodes-base.googleDriveTrigger", "position": [ 0, -96 ], "parameters": { "event": "fileCreated", "options": {}, "pollTimes": { "item": [ { "mode": "everyMinute" } ] }, "triggerOn": "specificFolder", "folderToWatch": { "__rl": true, "mode": "list", "value": "1KyX5RGqeF7v0sAvvaoBnJPTQoq4aOHLz", "cachedResultUrl": "https://drive.google.com/drive/folders/1KyX5RGqeF7v0sAvvaoBnJPTQoq4aOHLz", "cachedResultName": "HR auto" } }, "credentials": { "googleDriveOAuth2Api": { "id": "credential-id", "name": "googleDriveOAuth2Api Credential" } }, "typeVersion": 1 }, { "id": "dabfc101-cfa3-43e6-b568-320c1c2d3e92", "name": "Download resume", "type": "n8n-nodes-base.googleDrive", "position": [ 224, -96 ], "parameters": { "fileId": { "__rl": true, "mode": "url", "value": "={{ $json.webViewLink }}" }, "options": {}, "operation": "download" }, "credentials": { "googleDriveOAuth2Api": { "id": "credential-id", "name": "googleDriveOAuth2Api Credential" } }, "typeVersion": 3 }, { "id": "821c7e31-be41-4e6c-b58b-19a2cf880648", "name": "Extract text", "type": "n8n-nodes-base.extractFromFile", "position": [ 448, -96 ], "parameters": { "options": {}, "operation": "pdf" }, "typeVersion": 1 }, { "id": "78f8966f-dbeb-4773-b198-9dfb13defd35", "name": "Calculate avg span", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 672, -96 ], "parameters": { "text": "={{ $json.text }}", "options": { "systemMessage": "You are given resume text as input. Your task is to extract employment experiences and return ONLY the average tenure (in months) across those experiences as a single number.\n\nInstructions:\n- Scope: Consider only professional employment entries under Experience (exclude Education, Projects, Achievements, Certifications, Skills, Languages).\n- Grouping: Treat each distinct job/employer entry as one experience. If multiple roles at the same employer have separate date ranges, treat each as separate experiences.\n- Dates:\n - Parse start and end dates in formats like \"MMM YYYY\", \"Month YYYY\", \"YYYY\", or date ranges using -, –, —.\n - If the end date is \"Present\"/current, use today's date.\n - If a date has only a year, assume the month as January.\n - If a start or end month/day is missing, default to the first day of that month.\n- Duration Calculation:\n - Compute the difference in full months for each experience (start inclusive, end exclusive).\n - Do not deduct overlaps; each listed experience is counted independently.\n - Exclude entries without any valid start date.\n- Averaging:\n - Compute the arithmetic mean of the months across all valid experiences.\n - Round to the nearest whole month (standard rounding).\n\n" }, "promptType": "define", "hasOutputParser": true }, "typeVersion": 2.2 }, { "id": "5e697388-e418-495e-b91f-5eba9deb0427", "name": "Logic", "type": "n8n-nodes-base.if", "position": [ 1024, -96 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "21c98617-ba35-4906-afd0-38a82211dbe7", "operator": { "type": "number", "operation": "lt" }, "leftValue": "={{ $json.output.average.toNumber() }}", "rightValue": 12 } ] } }, "typeVersion": 2.2 }, { "id": "93b32316-47a7-4769-8a82-289a4c66c73f", "name": "Create email", "type": "n8n-nodes-base.code", "position": [ 1248, -96 ], "parameters": { "jsCode": "// filename: n8n-code-node-attrition-email.js\n// This Code node builds an email for each item indicating high attrition risk.\n// Output fields:\n// - emailSubject\n// - emailBody\n// - emailTo (optional if you want to set here)\n// - emailCc (optional)\n// - emailMetadata (structured data for downstream logging)\n\nfunction formatDate(dateStr) {\n if (!dateStr) return \"N/A\";\n const d = new Date(dateStr);\n if (isNaN(d.getTime())) return dateStr;\n // Format as YYYY-MM-DD for clarity\n return `${d.getFullYear()}-${String(d.getMonth() + 1).padStart(2, \"0\")}-${String(d.getDate()).padStart(2, \"0\")}`;\n}\n\nfunction clampScore(score) {\n const n = Number(score);\n if (Number.isNaN(n)) return null;\n return Math.max(0, Math.min(100, n));\n}\n\nfunction classifyRisk(score) {\n if (score == null) return \"Unknown\";\n if (score >= 80) return \"High\";\n if (score >= 50) return \"Medium\";\n return \"Low\";\n}\n\nfunction bulletList(items) {\n if (!Array.isArray(items) || items.length === 0) return \"- None\";\n return items.map(s => `- ${String(s).trim() || \"N/A\"}`).join(\"\\n\");\n}\n\nconst outputs = [];\n\nfor (const item of $input.all()) {\n const j = item.json || {};\n\n const personName = j.personName || j.name || \"The employee\";\n const role = j.role || \"N/A\";\n const department = j.department || null;\n const managerName = j.managerName || \"HR/People Ops\";\n const riskScoreRaw = j.riskScore ?? j.attritionRiskScore;\n const riskScore = clampScore(riskScoreRaw);\n const riskLevel = classifyRisk(riskScore);\n const signals = Array.isArray(j.signals) ? j.signals : [];\n const lastEngagementDate = formatDate(j.lastEngagementDate || j.lastCheckInDate);\n const recommendedActions = Array.isArray(j.recommendedActions) ? j.recommendedActions : [\n \"Schedule a 1:1 check‑in within the next 3–5 days\",\n \"Offer growth or role‑clarity conversation\",\n \"Review workload and compensation alignment\",\n ];\n\n // Email routing (optional; you can also set these in the Email node)\n const emailTo = j.emailTo || j.managerEmail || j.hrEmail || \"\";\n const emailCc = j.emailCc || \"\";\n\n // Compose subject\n const subjectParts = [\n \"[Attrition Alert]\",\n personName !== \"The employee\" ? personName : \"Employee\",\n riskLevel !== \"Unknown\" ? `– ${riskLevel} Risk (${riskScore}%)` : \"– Risk Review\",\n ];\n const emailSubject = subjectParts.filter(Boolean).join(\" \");\n\n // Compose body (plain text; you can convert to HTML if preferred)\n const headerLine = `${personName} ${role !== \"N/A\" ? `(${role})` : \"\"}${department ? `, ${department}` : \"\"}`;\n const riskLine = `Attrition Risk: ${riskLevel}${riskScore != null ? ` (${riskScore}%)` : \"\"}`;\n const signalsBlock = bulletList(signals);\n const actionsBlock = bulletList(recommendedActions);\n\n const emailBody =\n`Hi ${managerName},\n\nThis is a heads‑up that ${headerLine} may be at risk of leaving soon. ${riskLine}.\n\nKey signals observed:\n${signalsBlock}\n\nLast engagement/check‑in: ${lastEngagementDate}\n\nSuggested next steps:\n${actionsBlock}\n\nPlease prioritize a supportive outreach. If helpful, we can prepare a retention plan (growth discussion, workload review, recognition, and role clarity).\n\nThanks,\nPeople Analytics\n`;\n\n outputs.push({\n json: {\n ...j,\n emailSubject,\n emailBody,\n emailTo,\n emailCc,\n emailMetadata: {\n personName,\n role,\n department,\n managerName,\n riskScore,\n riskLevel,\n signals,\n lastEngagementDate,\n recommendedActions,\n generatedAt: new Date().toISOString(),\n },\n },\n });\n}\n\nreturn outputs;\n" }, "typeVersion": 2 }, { "id": "cc3e9d3e-4a29-4125-83bd-c43770219fca", "name": "Send email to hr", "type": "n8n-nodes-base.gmail", "position": [ 1472, -96 ], "webhookId": "15c04fae-06e4-41c8-860d-020d07ca31f6", "parameters": { "sendTo": "user@example.com", "message": "={{ $json.emailBody }}", "options": {}, "subject": "={{ $json.emailSubject }}" }, "credentials": { "gmailOAuth2": { "id": "credential-id", "name": "gmailOAuth2 Credential" } }, "typeVersion": 2.1 }, { "id": "860057d2-af64-4ea5-8df5-1945f27b52d2", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -336, -128 ], "parameters": { "content": "## Trigger for new resume  \nStarts the workflow when a new resume file is added (e.g., to storage or inbox)." }, "typeVersion": 1 }, { "id": "ce4d511d-b14d-4a13-ac97-a319e90b2d2a", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 128, -304 ], "parameters": { "content": "## Download resume  \nFetches the resume file from the source and makes it available for processing." }, "typeVersion": 1 }, { "id": "dcf63ce0-b0f0-4ede-9c95-c7c37814b028", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 384, 80 ], "parameters": { "content": "## Extract text  \nPulls readable text from the downloaded resume using a PDF extraction step." }, "typeVersion": 1 }, { "id": "114d70ee-1e49-41f4-937b-6b8e183d787b", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 656, -304 ], "parameters": { "content": "## Chat Model  \nUses Azure OpenAI Chat to analyze or summarize the extracted resume content." }, "typeVersion": 1 }, { "id": "b5ee6f06-3ac8-402a-9051-f5ee0aaae094", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ 960, 96 ], "parameters": { "content": "## Logic  \nApplies conditional checks and routing (true/false) based on parsed results." }, "typeVersion": 1 }, { "id": "e7fcd240-e008-419f-ac1b-e45593f3c037", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 1168, -320 ], "parameters": { "content": "## Create email  \nGenerates a tailored email draft to the candidate or HR using the parsed data." }, "typeVersion": 1 }, { "id": "e8aae9bd-426b-4020-8b47-f48bc35b1ae3", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [ 1408, 96 ], "parameters": { "content": "## Send email to hr  \nSends the composed message to HR via the configured email service." }, "typeVersion": 1 } ], "active": false, "pinData": {}, "settings": { "executionOrder": "v1" }, "versionId": "a93f6021-cc9d-4249-b529-62a19a79292a", "connections": { "Logic": { "main": [ [ { "node": "Create email", "type": "main", "index": 0 } ] ] }, "Create email": { "main": [ [ { "node": "Send email to hr", "type": "main", "index": 0 } ] ] }, "Extract text": { "main": [ [ { "node": "Calculate avg span", "type": "main", "index": 0 } ] ] }, "Download resume": { "main": [ [ { "node": "Extract text", "type": "main", "index": 0 } ] ] }, "Calculate avg span": { "main": [ [ { "node": "Logic", "type": "main", "index": 0 } ] ] }, "Trigger for new resume": { "main": [ [ { "node": "Download resume", "type": "main", "index": 0 } ] ] }, "Azure OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "Calculate avg span", "type": "ai_languageModel", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "Calculate avg span", "type": "ai_outputParser", "index": 0 } ] ] } } }, "lastUpdatedBy": 1, "workflowInfo": { "nodeCount": 16, "nodeTypes": { "n8n-nodes-base.if": { "count": 1 }, "n8n-nodes-base.code": { "count": 1 }, "n8n-nodes-base.gmail": { "count": 1 }, "n8n-nodes-base.stickyNote": { "count": 7 }, "n8n-nodes-base.googleDrive": { "count": 1 }, "@n8n/n8n-nodes-langchain.agent": { "count": 1 }, "n8n-nodes-base.extractFromFile": { "count": 1 }, "n8n-nodes-base.googleDriveTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi": { "count": 1 }, "@n8n/n8n-nodes-langchain.outputParserStructured": { "count": 1 } } }, "status": "published", "user": { "name": "Rahul Joshi", "username": "rahul08", "bio": "Rahul Joshi is a seasoned technology leader specializing in the n8n automation tool and AI-driven workflow automation. With deep expertise in building open-source workflow automation and self-hosted automation platforms, he helps organizations eliminate manual processes through intelligent n8n ai agent automation solutions.\n\n", "verified": true, "links": [ "https://www.linkedin.com/in/callrahul/" ], "avatar": "https://gravatar.com/avatar/b6cf57822463143589b36ada06fbf6cb1509223a740fae3160b28f1ce41ccc12?r=pg&d=retro&size=200" }, "nodes": [ { "id": 20, "icon": "fa:map-signs", "name": "n8n-nodes-base.if", "codex": { "data": { "alias": [ "Router", "Filter", "Condition", "Logic", "Boolean", "Branch" ], "details": "The IF node can be used to implement binary conditional logic in your workflow. You can set up one-to-many conditions to evaluate each item of data being inputted into the node. That data will either evaluate to TRUE or FALSE and route out of the node accordingly.\n\nThis node has multiple types of conditions: Bool, String, Number, and Date & Time.", "resources": { "generic": [ { "url": "https://n8n.io/blog/learn-to-automate-your-factorys-incident-reporting-a-step-by-step-guide/", "icon": "🏭", "label": "Learn to Automate Your Factory's Incident Reporting: A Step by Step Guide" }, { "url": "https://n8n.io/blog/2021-the-year-to-automate-the-new-you-with-n8n/", "icon": "☀️", "label": "2021: The Year to Automate the New You with n8n" }, { "url": "https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/", "icon": "🧬", "label": "Why business process automation with n8n can change your daily life" }, { "url": "https://n8n.io/blog/create-a-toxic-language-detector-for-telegram/", "icon": "🤬", "label": "Create a toxic language detector for Telegram in 4 step" }, { "url": "https://n8n.io/blog/no-code-ecommerce-workflow-automations/", "icon": "store", "label": "6 e-commerce workflows to power up your Shopify s" }, { "url": "https://n8n.io/blog/how-to-build-a-low-code-self-hosted-url-shortener/", "icon": "🔗", "label": "How to build a low-code, self-hosted URL shortener in 3 steps" }, { "url": "https://n8n.io/blog/automate-your-data-processing-pipeline-in-9-steps-with-n8n/", "icon": "⚙️", "label": "Automate your data processing pipeline in 9 steps" }, { "url": "https://n8n.io/blog/how-to-get-started-with-crm-automation-and-no-code-workflow-ideas/", "icon": "👥", "label": "How to get started with CRM automation (with 3 no-code workflow ideas" }, { "url": "https://n8n.io/blog/5-tasks-you-can-automate-with-notion-api/", "icon": "⚡️", "label": "5 tasks you can automate with the new Notion API " }, { "url": "https://n8n.io/blog/automate-google-apps-for-productivity/", "icon": "💡", "label": "15 Google apps you can combine and automate to increase productivity" }, { "url": "https://n8n.io/blog/automation-for-maintainers-of-open-source-projects/", "icon": "🏷️", "label": "How to automatically manage contributions to open-source projects" }, { "url": "https://n8n.io/blog/how-uproc-scraped-a-multi-page-website-with-a-low-code-workflow/", "icon": " 🕸️", "label": "How uProc scraped a multi-page website with a low-code workflow" }, { "url": "https://n8n.io/blog/5-workflow-automations-for-mattermost-that-we-love-at-n8n/", "icon": "🤖", "label": "5 workflow automations for Mattermost that we love at n8n" }, { "url": "https://n8n.io/blog/why-this-product-manager-loves-workflow-automation-with-n8n/", "icon": "🧠", "label": "Why this Product Manager loves workflow automation with n8n" }, { "url": "https://n8n.io/blog/sending-automated-congratulations-with-google-sheets-twilio-and-n8n/", "icon": "🙌", "label": "Sending Automated Congratulations with Google Sheets, Twilio, and n8n " }, { "url": "https://n8n.io/blog/how-to-set-up-a-ci-cd-pipeline-with-no-code/", "icon": "🎡", "label": "How to set up a no-code CI/CD pipeline with GitHub and TravisCI" }, { "url": "https://n8n.io/blog/benefits-of-automation-and-n8n-an-interview-with-hubspots-hugh-durkin/", "icon": "🎖", "label": "Benefits of automation and n8n: An interview with HubSpot's Hugh Durkin" }, { "url": "https://n8n.io/blog/aws-workflow-automation/", "label": "7 no-code workflow automations for Amazon Web Services" } ], "primaryDocumentation": [ { "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.if/" } ] }, "categories": [ "Core Nodes" ], "nodeVersion": "1.0", "codexVersion": "1.0", "subcategories": { "Core Nodes": [ "Flow" ] } } }, "group": "[\"transform\"]", "defaults": { "name": "If", "color": "#408000" }, "iconData": { "icon": "map-signs", "type": "icon" }, "displayName": "If", "typeVersion": 2, "nodeCategories": [ { "id": 9, "name": "Core Nodes" } ] }, { "id": 58, "icon": "file:googleDrive.svg", "name": "n8n-nodes-base.googleDrive", "codex": { "data": { "resources": { "generic": [ { "url": "https://n8n.io/blog/your-business-doesnt-need-you-to-operate/", "icon": " 🖥️", "label": "Hey founders! 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Your business doesn't need you to operate" }, { "url": "https://n8n.io/blog/using-automation-to-boost-productivity-in-the-workplace/", "icon": "💪", "label": "Using Automation to Boost Productivity in the Workplace" } ], "primaryDocumentation": [ { "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.gmail/" } ], "credentialDocumentation": [ { "url": "https://docs.n8n.io/integrations/builtin/credentials/google/oauth-single-service/" } ] }, "categories": [ "Communication", "HITL" ], "nodeVersion": "1.0", "codexVersion": "1.0", "subcategories": { "HITL": [ "Human in the Loop" ] } } }, "group": "[\"transform\"]", "defaults": { "name": "Gmail" }, "iconData": { "type": "file", "fileBuffer": 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