{ "name": "Company News Intelligence Analyzer", "nodes": [ { "parameters": {}, "id": "02140815-edd9-44ad-858b-771917a74dff", "name": "When clicking β€˜Test workflow’", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [ 4240, -784 ] }, { "parameters": { "rule": { "interval": [ { "triggerAtHour": 2 } ] } }, "id": "01e88508-3b43-419f-9a05-ab569a33d844", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ 4448, -608 ] }, { "parameters": { "aggregate": "aggregateAllItemData", "options": {} }, "id": "70d87a82-a2c3-44c2-8ab4-5529578d32b1", "name": "Aggregate Results", "type": "n8n-nodes-base.aggregate", "typeVersion": 1, "position": [ 6352, -704 ] }, { "parameters": { "jsCode": "// Build full nested Intelligence Report structure\nconst inputData = $input.first().json.data || $input.all().map(i => i.json);\nconst companyName = $(\"Split In Batches1\").first().json.company_name || \"Company\";\n\nconst report = {\n company_intelligence_report: {\n company_name: companyName,\n executive_summary: { \n overall_sentiment: \"neutral\", \n top_event_types: [],\n article_count: inputData.length \n },\n corporate_events: { \n acquisitions: [], \n funding_rounds: [], \n layoffs: [], \n partnerships: [] \n },\n people: { \n leadership_changes: [], \n key_people: [] \n },\n financial_metrics: {},\n employees: { \n headcount_changes: [], \n hiring_areas: [] \n },\n key_insights: [],\n article_analyses: []\n }\n};\n\nlet sentimentList = [];\nlet eventSet = new Set();\nlet hiringSet = new Set();\n\ninputData.forEach(item => {\n const a = item.analysis || {};\n const art = item.article || {};\n \n if (a.sentiment) sentimentList.push(a.sentiment);\n if (a.event_types) a.event_types.forEach(e => eventSet.add(e));\n if (a.acquisitions) report.company_intelligence_report.corporate_events.acquisitions.push(...a.acquisitions);\n if (a.funding) report.company_intelligence_report.corporate_events.funding_rounds.push(a.funding);\n if (a.layoffs) report.company_intelligence_report.corporate_events.layoffs.push(a.layoffs);\n if (a.partnerships) report.company_intelligence_report.corporate_events.partnerships.push(...a.partnerships);\n if (a.leadership_changes) report.company_intelligence_report.people.leadership_changes.push(...a.leadership_changes);\n if (a.employees?.hiring_areas) a.employees.hiring_areas.forEach(h => hiringSet.add(h));\n if (a.key_insights) report.company_intelligence_report.key_insights.push(...a.key_insights);\n \n if (a.financial_metrics) {\n Object.assign(report.company_intelligence_report.financial_metrics, a.financial_metrics);\n }\n\n report.company_intelligence_report.article_analyses.push({\n title: art.title,\n summary: a.summary,\n url: art.url\n });\n});\n\n// Final formatting\nreport.company_intelligence_report.executive_summary.top_event_types = Array.from(eventSet);\nreport.company_intelligence_report.employees.hiring_areas = Array.from(hiringSet);\nreport.company_intelligence_report.key_insights = [...new Set(report.company_intelligence_report.key_insights)].slice(0, 10);\n\n// Simple sentiment frequency\nif (sentimentList.length > 0) {\n const counts = sentimentList.reduce((acc, s) => ({ ...acc, [s]: (acc[s] || 0) + 1 }), {});\n report.company_intelligence_report.executive_summary.overall_sentiment = Object.keys(counts).reduce((a, b) => counts[a] > counts[b] ? a : b);\n}\n\n// Build Telegram message string\nlet telegramMsg = `πŸš€ Intelligence Report: ${companyName}\\n`;\ntelegramMsg += `───────────────────\\n`;\ntelegramMsg += `πŸ“Š Summary: ${report.company_intelligence_report.executive_summary.overall_sentiment.toUpperCase()} sentiment across ${inputData.length} sources.\\n`;\n\nif (report.company_intelligence_report.corporate_events.layoffs.length > 0) {\n telegramMsg += `\\n⚠️ Layoffs Detected: Impact reported in news cycle.\\n`;\n}\n\ntelegramMsg += `\\nπŸ’‘ Top Insights:\\n`;\nreport.company_intelligence_report.key_insights.slice(0, 5).forEach(i => telegramMsg += `β€’ ${i}\\n`);\n\ntelegramMsg += `\\nView Full Feed`;\n\nreturn [{ json: { ...report, telegram_message: telegramMsg } }];" }, "id": "b308cb79-9b8e-4543-842f-e88b58ef88c2", "name": "Format Intelligence Report", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 6576, -704 ] }, { "parameters": { "chatId": "-1003540593724", "text": "={{ $json.telegram_message }}", "additionalFields": { "parse_mode": "HTML" } }, "id": "42dbd800-711e-481f-a307-6cf9351a9614", "name": "Telegram", "type": "n8n-nodes-base.telegram", "typeVersion": 1.1, "position": [ 6800, -704 ], "webhookId": "4912a3f3-67da-40da-881c-0582a4630174", "credentials": { "telegramApi": { "id": "cBUm2qz5RbGf3sq5", "name": "News Analyzer" } } }, { "parameters": { "jsCode": "// --- DYNAMIC COMPANY LIST ---\nconst companies = ['NVIDIA', 'Microsoft', 'OpenAI', 'Tesla'];\n\nreturn companies.map(name => ({\n json: {\n company_name: name,\n max_articles: 5\n }\n}));" }, "id": "2ba30de4-8ba6-4860-9929-a3f2c00349b8", "name": "Dynamic Company List1", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 4752, -704 ] }, { "parameters": { "options": {} }, "id": "1f98b0ad-6ee8-4974-bab1-b86c7d486d61", "name": "Split In Batches1", "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ 4992, -704 ] }, { "parameters": { "url": "=https://news.google.com/rss/search?q={{ encodeURIComponent($json.company_name + ' company news') }}&hl=en-US&gl=US&ceid=US:en", "options": { "response": { "response": { "responseFormat": "text" } } } }, "id": "0d59f527-03ff-4bd4-a4e3-48b04e98e7d3", "name": "Fetch Google News RSS1", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 5216, -704 ] }, { "parameters": { "jsCode": "const rssText = $input.first().json.data;\nconst company = $(\"Split In Batches1\").first().json.company_name;\nconst items = [];\nconst itemRegex = /(.*?)<\\/item>/gs;\nlet match;\n\nif (!rssText) return [];\n\nwhile ((match = itemRegex.exec(rssText)) !== null && items.length < 5) {\n const itemXml = match[1];\n const getField = (tag) => {\n const m = itemXml.match(new RegExp(`<${tag}[^>]*>([\\\\s\\\\S]*?)<\\/${tag}>`, 'i'));\n return m ? m[1].replace(//g, '').trim() : '';\n };\n items.push({ json: { company_name: company, title: getField('title'), url: getField('link'), description: getField('description').replace(/<[^>]+>/g, ' '), source: getField('source') } });\n}\nreturn items;" }, "id": "c0c10487-551a-4030-bbd5-394a672f3caa", "name": "Parse RSS Articles1", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5440, -704 ] }, { "parameters": { "jsCode": "// Fixed Prompt Generator for full intelligence extraction\nreturn $input.all().map(item => {\n const article = item.json;\n const prompt = `You are a corporate intelligence analyst. Analyze news for ${article.company_name}.\nArticle: ${article.title}\nContent: ${article.description}\n\nReturn ONLY a JSON object with this exact structure (use null for unknown values):\n{\n \"summary\": \"2 sentence summary\",\n \"event_types\": [\"acquisition\", \"funding\", \"layoff\", \"leadership\", \"partnership\", \"earnings\", \"product\"],\n \"acquisitions\": [{\"target\": \"name\", \"value\": \"value\"}],\n \"funding\": {\"round\": \"A/B/C\", \"amount\": \"val\", \"valuation\": \"val\"},\n \"layoffs\": {\"count\": null, \"reason\": \"str\"},\n \"leadership_changes\": [{\"person\": \"name\", \"new_role\": \"title\", \"action\": \"appointed/left\"}],\n \"financial_metrics\": {\"revenue\": \"val\", \"profit\": \"val\", \"market_cap\": \"val\"},\n \"employees\": {\"headcount_change\": \"increase/decrease\", \"hiring_areas\": [\"tech\", \"sales\"]},\n \"sentiment\": \"positive/negative/neutral\",\n \"key_insights\": [\"insight 1\", \"insight 2\"]\n}`;\n\n return {\n json: {\n prompt,\n article_data: article\n }\n };\n});" }, "id": "8e9196a7-3eb5-4225-b2e4-f2519cfc5a2a", "name": "Prepare Gemini Prompt1", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 5648, -704 ] }, { "parameters": { "jsCode": "// Robust Gemini Parser with meta-data recovery\nconst inputJson = $input.first().json;\nconst responseText = inputJson.text || inputJson.output || inputJson.content?.parts?.[0]?.text || \"\";\nconst articleData = $input.first().json.article_data || $item(0).$node[\"Prepare Gemini Prompt1\"].json.article_data;\n\nlet analysis = {};\ntry {\n const jsonMatch = responseText.match(/\\{[\\s\\S]*\\}/);\n if (jsonMatch) {\n const cleaned = jsonMatch[0].replace(/[\\u0000-\\u001F\\u007F-\\u009F]/g, \"\");\n analysis = JSON.parse(cleaned);\n } else {\n analysis = { error: \"No JSON found\", raw: responseText.substring(0, 100) };\n }\n} catch (e) {\n analysis = { error: \"Parse failed\", raw: responseText.substring(0, 100) };\n}\n\nreturn [{ json: { article: articleData, analysis } }];" }, "id": "1071cff1-5de5-4860-bb28-eccce5d0d185", "name": "Parse Gemini Response1", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 6128, -704 ] }, { "parameters": { "model": "gemma4:31b-cloud", "options": { "temperature": 0 } }, "id": "e9781f1f-6195-4d2f-bd00-cdcbda18e87b", "name": "Ollama Model", "type": "@n8n/n8n-nodes-langchain.lmChatOllama", "typeVersion": 1, "position": [ 5824, -448 ], "credentials": { "ollamaApi": { "id": "9k920xXzUND3cX1z", "name": "Ollama account" } } }, { "parameters": { "modelId": { "__rl": true, "value": "gemma4:31b-cloud", "mode": "list", "cachedResultName": "gemma4:31b-cloud" }, "messages": { "values": [ { "content": "={{ $json.prompt }}" } ] }, "options": {} }, "type": "@n8n/n8n-nodes-langchain.ollama", "typeVersion": 1, "position": [ 5808, -784 ], "id": "e7d89d03-ce2e-4673-b6fb-88359f177a97", "name": "Message a model", "credentials": { "ollamaApi": { "id": "9k920xXzUND3cX1z", "name": "Ollama account" } } } ], "pinData": {}, "connections": { "When clicking β€˜Test workflow’": { "main": [ [ { "node": "Dynamic Company List1", "type": "main", "index": 0 } ] ] }, "Schedule Trigger": { "main": [ [ { "node": "Dynamic Company List1", "type": "main", "index": 0 } ] ] }, "Aggregate Results": { "main": [ [ { "node": "Format Intelligence Report", "type": "main", "index": 0 } ] ] }, "Format Intelligence Report": { "main": [ [ { "node": "Telegram", "type": "main", "index": 0 } ] ] }, "Dynamic Company List1": { "main": [ [ { "node": "Split In Batches1", "type": "main", "index": 0 } ] ] }, "Split In Batches1": { "main": [ [ { "node": "Fetch Google News RSS1", "type": "main", "index": 0 } ], [ { "node": "Aggregate Results", "type": "main", "index": 0 } ] ] }, "Fetch Google News RSS1": { "main": [ [ { "node": "Parse RSS Articles1", "type": "main", "index": 0 } ] ] }, "Parse RSS Articles1": { "main": [ [ { "node": "Prepare Gemini Prompt1", "type": "main", "index": 0 } ] ] }, "Prepare Gemini Prompt1": { "main": [ [ { "node": "Message a model", "type": "main", "index": 0 } ] ] }, "Parse Gemini Response1": { "main": [ [ { "node": "Split In Batches1", "type": "main", "index": 0 } ] ] }, "Message a model": { "main": [ [ { "node": "Parse Gemini Response1", "type": "main", "index": 0 } ] ] } }, "active": true, "settings": { "executionOrder": "v1", "binaryMode": "separate", "saveManualExecutions": true, "callerPolicy": "workflowsFromSameOwner", "availableInMCP": false }, "versionId": "c3421a8d-f0eb-431f-8665-a36c0bca1bcf", "meta": { "templateCredsSetupCompleted": true, "instanceId": "e09d1f0f8b9f78e80ac70b6dc8726dd263b0b6ffc4300b0eee3b58f6da316f29" }, "id": "EJsPsu4Y3hPSnemm", "tags": [ { "updatedAt": "2026-03-18T22:49:30.672Z", "createdAt": "2026-03-18T22:49:30.672Z", "id": "w3YWknU07IFxAbCs", "name": "News" } ] }