diff --git a/ceo-ai-enterprise-workflow-v3.json b/ceo-ai-enterprise-workflow-v3.json
new file mode 100644
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+++ b/ceo-ai-enterprise-workflow-v3.json
@@ -0,0 +1,421 @@
+{
+  "name": "CEO AI Enterprise Workflow v3",
+  "nodes": [
+    {
+      "parameters": {
+        "httpMethod": "POST",
+        "path": "enterprise-request",
+        "responseMode": "responseNode",
+        "options": {}
+      },
+      "id": "node-entry-01",
+      "name": "Webhook Input",
+      "type": "n8n-nodes-base.webhook",
+      "typeVersion": 1,
+      "position": [80, 500]
+    },
+    {
+      "parameters": {
+        "jsCode": "const input = $input.first().json;\nconst userProblem = input.problem || input.body?.problem || '';\nconst userEmail = input.email || input.body?.email || 'mail.mail@gmail.com';\nconst sendMail = input.send_mail || input.body?.send_mail || false;\nconst mailCategory = input.mail_category || input.body?.mail_category || 'Autres';\nconst transcription = input.transcription || input.body?.transcription || '';\n\nconst text = userProblem.toLowerCase();\nconst devRequested = ['développement','prototype','code','pipeline','architecture logicielle','software'].some(k => text.includes(k));\nconst writingRequested = ['compte rendu','compte-rendu','réunion','meeting','rédaction','rapport','transcription'].some(k => text.includes(k)) || transcription.length > 0;\nconst deepLearningFocus = ['deep learning','deeplearning','neural network','transformer','llm'].some(k => text.includes(k));\nconst symbolicFocus = ['symbolique','logic','ontologie','knowledge graph','règle'].some(k => text.includes(k));\n\nreturn [{\n  json: {\n    user_problem: userProblem,\n    user_email: userEmail,\n    send_mail_requested: sendMail,\n    mail_category: mailCategory,\n    transcription,\n    ceo_instructions: {\n      reformulated_objective: 'Objectif de recherche : ' + userProblem,\n      dev_requested: devRequested,\n      writing_requested: writingRequested,\n      deep_learning_focus: deepLearningFocus,\n      symbolic_focus: symbolicFocus,\n      mission_plan: [\n        '1. Recherche DBLP multi-agent',\n        '2. Sous-cellule Neuro-Symbolique (Symbolique + Deep Learning)',\n        '3. Consolidation + présentation article + trame PPT',\n        devRequested ? '4. Activation équipe Développement + Testing multi-agent' : '4. Développement non activé',\n        writingRequested ? '5. Activation équipe Communication' : '5. Communication non activée'\n      ],\n      categories: ['Green IT','IA neuro_symbolique','architecture solution','scoring'],\n      source_constraint: 'DBLP uniquement — https://dblp.org'\n    },\n    timestamp: new Date().toISOString()\n  }\n}];"
+      },
+      "id": "node-ceo-01",
+      "name": "CEO Agent",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [340, 500]
+    },
+    {
+      "parameters": {
+        "jsCode": "const ceo = $input.first().json;\nconst p = ceo.user_problem;\nreturn [\n  { json: { ...ceo, research_task: { agent_name: 'Research Agent 1 - Green IT', target_category: 'Green IT', dblp_query: p + ' green computing sustainability energy efficiency' } } },\n  { json: { ...ceo, research_task: { agent_name: 'Research Agent 2A - Symbolique', target_category: 'IA neuro_symbolique', dblp_query: p + ' symbolic AI logic ontology knowledge graph reasoning' } } },\n  { json: { ...ceo, research_task: { agent_name: 'Research Agent 2B - Deep Learning', target_category: 'IA neuro_symbolique', dblp_query: p + ' deep learning neural network transformer representation learning' } } },\n  { json: { ...ceo, research_task: { agent_name: 'Research Agent 3 - Architecture', target_category: 'architecture solution', dblp_query: p + ' solution architecture distributed systems cloud microservices' } } },\n  { json: { ...ceo, research_task: { agent_name: 'Research Agent 4 - Scoring', target_category: 'scoring', dblp_query: p + ' scoring evaluation metrics benchmark assessment' } } }\n];"
+      },
+      "id": "node-r-01",
+      "name": "Research Manager",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [620, 500]
+    },
+    {
+      "parameters": {
+        "url": "https://dblp.org/search/publ/api",
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            { "name": "q", "value": "={{ $json.research_task.dblp_query }}" },
+            { "name": "format", "value": "json" },
+            { "name": "h", "value": "10" }
+          ]
+        },
+        "options": {}
+      },
+      "id": "node-r-02",
+      "name": "Research Agent 1 - Green IT",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4,
+      "position": [900, 120]
+    },
+    {
+      "parameters": {
+        "url": "https://dblp.org/search/publ/api",
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            { "name": "q", "value": "={{ $json.research_task.dblp_query }}" },
+            { "name": "format", "value": "json" },
+            { "name": "h", "value": "10" }
+          ]
+        },
+        "options": {}
+      },
+      "id": "node-r-03a",
+      "name": "Research Agent 2A - Symbolique",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4,
+      "position": [900, 260]
+    },
+    {
+      "parameters": {
+        "url": "https://dblp.org/search/publ/api",
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            { "name": "q", "value": "={{ $json.research_task.dblp_query }}" },
+            { "name": "format", "value": "json" },
+            { "name": "h", "value": "10" }
+          ]
+        },
+        "options": {}
+      },
+      "id": "node-r-03b",
+      "name": "Research Agent 2B - Deep Learning",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4,
+      "position": [900, 400]
+    },
+    {
+      "parameters": {
+        "url": "https://dblp.org/search/publ/api",
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            { "name": "q", "value": "={{ $json.research_task.dblp_query }}" },
+            { "name": "format", "value": "json" },
+            { "name": "h", "value": "10" }
+          ]
+        },
+        "options": {}
+      },
+      "id": "node-r-04",
+      "name": "Research Agent 3 - Architecture",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4,
+      "position": [900, 540]
+    },
+    {
+      "parameters": {
+        "url": "https://dblp.org/search/publ/api",
+        "sendQuery": true,
+        "queryParameters": {
+          "parameters": [
+            { "name": "q", "value": "={{ $json.research_task.dblp_query }}" },
+            { "name": "format", "value": "json" },
+            { "name": "h", "value": "10" }
+          ]
+        },
+        "options": {}
+      },
+      "id": "node-r-05",
+      "name": "Research Agent 4 - Scoring",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4,
+      "position": [900, 680]
+    },
+    {
+      "parameters": { "mode": "append", "options": {} },
+      "id": "node-r-06",
+      "name": "Merge Research Results",
+      "type": "n8n-nodes-base.merge",
+      "typeVersion": 2,
+      "position": [1160, 420]
+    },
+    {
+      "parameters": {
+        "jsCode": "const items = $input.all();\nconst seen = new Set();\nconst out = [];\nfor (const item of items) {\n  const raw = item.json;\n  const task = raw.research_task || {};\n  const hits = raw?.result?.hits?.hit || [];\n  for (const h of hits) {\n    const i = h.info || {};\n    const key = (i.title + (i.doi||'') + (i.key||'')).toLowerCase().replace(/\\s+/g,'');\n    if (!i.title || seen.has(key)) continue;\n    seen.add(key);\n    out.push({\n      title: i.title,\n      year: i.year || '',\n      authors: Array.isArray(i.authors?.author) ? i.authors.author.map(a => typeof a === 'string' ? a : (a.text || a['#text'] || '')) : [],\n      venue: i.venue || i.journal || '',\n      doi: i.doi || '',\n      dblp_key: i.key || '',\n      dblp_url: i.url || i.ee || '',\n      source: 'DBLP',\n      agent_name: task.agent_name,\n      category: task.target_category\n    });\n  }\n}\nreturn [{ json: { research_results: out, total_raw: out.length } }];"
+      },
+      "id": "node-r-07",
+      "name": "Consolidate Research Results",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1380, 420]
+    },
+    {
+      "parameters": {
+        "jsCode": "const data = $input.first().json;\nconst articles = data.research_results || [];\nconst presentation_articles = articles.map(a => ({\n  ...a,\n  summary: `Article: ${a.title}. Publication: ${a.venue} (${a.year || 'N/A'}).`,\n  slide_ideas: [\n    `Titre: ${a.title}`,\n    `Contexte problématique: relié à la demande utilisateur`,\n    `Apport principal: à discuter`,\n    `Source DBLP: ${a.dblp_url || 'N/A'}`\n  ]\n}));\nconst ppt_outline = {\n  title: 'Synthèse Recherche CEO',\n  slides: [\n    { section: 'Contexte & Objectif', ideas: ['Problématique client', 'Objectif reformulé'] },\n    { section: 'Top articles par catégorie', ideas: ['Green IT', 'IA Neuro-Symbolique', 'Architecture', 'Scoring'] },\n    { section: 'Recommandations', ideas: ['Actions court terme', 'Axes développement'] }\n  ]\n};\nreturn [{ json: { presentation_articles, ppt_outline, total: presentation_articles.length } }];"
+      },
+      "id": "node-r-08",
+      "name": "Article Presentation Agent",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1600, 420]
+    },
+    {
+      "parameters": {
+        "conditions": { "boolean": [ { "value1": "={{ $json.ceo_instructions.dev_requested }}", "value2": true } ] }
+      },
+      "id": "node-d-01",
+      "name": "IF Dev Requested?",
+      "type": "n8n-nodes-base.if",
+      "typeVersion": 1,
+      "position": [620, 840]
+    },
+    {
+      "parameters": {
+        "jsCode": "const ceo = $input.first().json;\nreturn [{ json: {\n  user_problem: ceo.user_problem,\n  dev_backlog: [\n    'Collecte DBLP et normalisation',\n    'Classification neuro-symbolique',\n    'Scoring et API de restitution'\n  ]\n} }];"
+      },
+      "id": "node-d-02",
+      "name": "Development Team Lead",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [860, 840]
+    },
+    {
+      "parameters": {
+        "jsCode": "const d = $input.first().json; return [{ json: { ai_specs: { owner: 'Dev Agent IA', tasks: ['modèle deep learning', 'module neuro-symbolique'] }, ctx: d } }];"
+      },
+      "id": "node-d-03a",
+      "name": "Dev Agent IA",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1120, 760]
+    },
+    {
+      "parameters": {
+        "jsCode": "const d = $input.first().json; return [{ json: { ds_specs: { owner: 'Dev Agent Data Scientist', tasks: ['features', 'scoring', 'benchmark'] }, ctx: d } }];"
+      },
+      "id": "node-d-03b",
+      "name": "Dev Agent Data Scientist",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1120, 920]
+    },
+    {
+      "parameters": { "mode": "append", "options": {} },
+      "id": "node-d-03m",
+      "name": "Merge Dev Agents",
+      "type": "n8n-nodes-base.merge",
+      "typeVersion": 2,
+      "position": [1360, 840]
+    },
+    {
+      "parameters": {
+        "jsCode": "const items = $input.all().map(i => i.json);\nreturn [{ json: { dev_outputs: items, handoff: 'to testing lead' } }];"
+      },
+      "id": "node-d-04",
+      "name": "Testing Team Lead",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1560, 840]
+    },
+    {
+      "parameters": { "jsCode": "return [{ json: { test_area: 'Papers & expériences', result: 'OK' } }];" },
+      "id": "node-d-05a",
+      "name": "Testing Agent - Papers",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1780, 680]
+    },
+    {
+      "parameters": { "jsCode": "return [{ json: { test_area: 'Deep Learning', result: 'OK' } }];" },
+      "id": "node-d-05b",
+      "name": "Testing Agent - Deep Learning",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1780, 800]
+    },
+    {
+      "parameters": { "jsCode": "return [{ json: { test_area: 'Symbolique', result: 'OK' } }];" },
+      "id": "node-d-05c",
+      "name": "Testing Agent - Symbolique",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1780, 920]
+    },
+    {
+      "parameters": { "jsCode": "return [{ json: { test_area: 'Neuro-Symbolique', result: 'OK' } }];" },
+      "id": "node-d-05d",
+      "name": "Testing Agent - Neuro-Symbolique",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1780, 1040]
+    },
+    {
+      "parameters": { "mode": "append", "options": {} },
+      "id": "node-d-06",
+      "name": "Merge Testing Results",
+      "type": "n8n-nodes-base.merge",
+      "typeVersion": 2,
+      "position": [2020, 840]
+    },
+    {
+      "parameters": {
+        "conditions": { "boolean": [ { "value1": "={{ $json.ceo_instructions.writing_requested }}", "value2": true } ] }
+      },
+      "id": "node-w-01",
+      "name": "IF Writing Requested?",
+      "type": "n8n-nodes-base.if",
+      "typeVersion": 1,
+      "position": [620, 1180]
+    },
+    {
+      "parameters": { "jsCode": "const ceo = $input.first().json; return [{ json: { transcription: ceo.transcription || '', problem: ceo.user_problem || '' } }];" },
+      "id": "node-w-02",
+      "name": "Communication Team Lead",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [860, 1180]
+    },
+    {
+      "parameters": { "jsCode": "const d=$input.first().json; return [{ json: { meeting_minutes: 'Compte rendu réunion généré', src: d.transcription } }];" },
+      "id": "node-w-03",
+      "name": "Meeting Minutes Agent",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1120, 1120]
+    },
+    {
+      "parameters": { "jsCode": "const d=$input.first().json; return [{ json: { general_writing: 'Rédaction hors recherche prête', topic: d.problem } }];" },
+      "id": "node-w-04",
+      "name": "General Writing Agent",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [1120, 1240]
+    },
+    {
+      "parameters": { "mode": "append", "options": {} },
+      "id": "node-w-05",
+      "name": "Merge Communication Outputs",
+      "type": "n8n-nodes-base.merge",
+      "typeVersion": 2,
+      "position": [1360, 1180]
+    },
+    {
+      "parameters": {
+        "jsCode": "const data = $('Article Presentation Agent').first().json;\nconst ceo = $('CEO Agent').first().json;\nlet dev = null; try { dev = $('Merge Testing Results').all().map(i => i.json); } catch(e) {}\nlet comm = null; try { comm = $('Merge Communication Outputs').all().map(i => i.json); } catch(e) {}\nconst articles = data.presentation_articles || [];\nconst html = `<h2>Résultats Recherche DBLP</h2><p>${ceo.user_problem}</p><p>Total: ${articles.length}</p>`;\nreturn [{ json: {\n  to: ceo.user_email || 'mail.mail@gmail.com',\n  subject: '[Recherche DBLP] Synthèse CEO',\n  email_html: html,\n  ppt_outline: data.ppt_outline,\n  dev_result: dev,\n  communication_result: comm,\n  send_mail_requested: ceo.send_mail_requested\n} }];"
+      },
+      "id": "node-m-01",
+      "name": "Assistant - Results Consolidation",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [2280, 620]
+    },
+    {
+      "parameters": {
+        "conditions": { "boolean": [ { "value1": "={{ $json.send_mail_requested }}", "value2": true } ] }
+      },
+      "id": "node-m-02",
+      "name": "IF Send Mail?",
+      "type": "n8n-nodes-base.if",
+      "typeVersion": 1,
+      "position": [2520, 620]
+    },
+    {
+      "parameters": {
+        "fromEmail": "assistante@enterprise.ai",
+        "toEmail": "={{ $json.to }}",
+        "subject": "={{ $json.subject }}",
+        "emailType": "html",
+        "message": "={{ $json.email_html }}",
+        "options": {}
+      },
+      "id": "node-m-03",
+      "name": "Send Email - Unified",
+      "type": "n8n-nodes-base.emailSend",
+      "typeVersion": 2,
+      "position": [2760, 560]
+    },
+    {
+      "parameters": {
+        "jsCode": "const data = $input.first().json;\nconst ceo = $('CEO Agent').first().json;\nreturn [{ json: {\n  timestamp: new Date().toISOString(),\n  status: 'COMPLETED',\n  user_problem: ceo.user_problem,\n  email_target: ceo.user_email || 'mail.mail@gmail.com',\n  mission_plan: ceo.ceo_instructions.mission_plan,\n  ppt_outline: data.ppt_outline,\n  dev_result: data.dev_result,\n  communication_result: data.communication_result\n} }];"
+      },
+      "id": "node-final-01",
+      "name": "Final CEO Synthesis",
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [3000, 680]
+    },
+    {
+      "parameters": {
+        "respondWith": "json",
+        "responseBody": "={{ JSON.stringify($json, null, 2) }}",
+        "options": {}
+      },
+      "id": "node-final-02",
+      "name": "Webhook Response",
+      "type": "n8n-nodes-base.respondToWebhook",
+      "typeVersion": 1,
+      "position": [3240, 680]
+    }
+  ],
+  "connections": {
+    "Webhook Input": { "main": [[{ "node": "CEO Agent", "type": "main", "index": 0 }]] },
+    "CEO Agent": { "main": [[
+      { "node": "Research Manager", "type": "main", "index": 0 },
+      { "node": "IF Dev Requested?", "type": "main", "index": 0 },
+      { "node": "IF Writing Requested?", "type": "main", "index": 0 }
+    ]] },
+    "Research Manager": { "main": [[
+      { "node": "Research Agent 1 - Green IT", "type": "main", "index": 0 },
+      { "node": "Research Agent 2A - Symbolique", "type": "main", "index": 0 },
+      { "node": "Research Agent 2B - Deep Learning", "type": "main", "index": 0 },
+      { "node": "Research Agent 3 - Architecture", "type": "main", "index": 0 },
+      { "node": "Research Agent 4 - Scoring", "type": "main", "index": 0 }
+    ]] },
+    "Research Agent 1 - Green IT": { "main": [[{ "node": "Merge Research Results", "type": "main", "index": 0 }]] },
+    "Research Agent 2A - Symbolique": { "main": [[{ "node": "Merge Research Results", "type": "main", "index": 1 }]] },
+    "Research Agent 2B - Deep Learning": { "main": [[{ "node": "Merge Research Results", "type": "main", "index": 2 }]] },
+    "Research Agent 3 - Architecture": { "main": [[{ "node": "Merge Research Results", "type": "main", "index": 3 }]] },
+    "Research Agent 4 - Scoring": { "main": [[{ "node": "Merge Research Results", "type": "main", "index": 4 }]] },
+    "Merge Research Results": { "main": [[{ "node": "Consolidate Research Results", "type": "main", "index": 0 }]] },
+    "Consolidate Research Results": { "main": [[{ "node": "Article Presentation Agent", "type": "main", "index": 0 }]] },
+
+    "IF Dev Requested?": { "main": [[{ "node": "Development Team Lead", "type": "main", "index": 0 }], []] },
+    "Development Team Lead": { "main": [[
+      { "node": "Dev Agent IA", "type": "main", "index": 0 },
+      { "node": "Dev Agent Data Scientist", "type": "main", "index": 0 }
+    ]] },
+    "Dev Agent IA": { "main": [[{ "node": "Merge Dev Agents", "type": "main", "index": 0 }]] },
+    "Dev Agent Data Scientist": { "main": [[{ "node": "Merge Dev Agents", "type": "main", "index": 1 }]] },
+    "Merge Dev Agents": { "main": [[{ "node": "Testing Team Lead", "type": "main", "index": 0 }]] },
+    "Testing Team Lead": { "main": [[
+      { "node": "Testing Agent - Papers", "type": "main", "index": 0 },
+      { "node": "Testing Agent - Deep Learning", "type": "main", "index": 0 },
+      { "node": "Testing Agent - Symbolique", "type": "main", "index": 0 },
+      { "node": "Testing Agent - Neuro-Symbolique", "type": "main", "index": 0 }
+    ]] },
+    "Testing Agent - Papers": { "main": [[{ "node": "Merge Testing Results", "type": "main", "index": 0 }]] },
+    "Testing Agent - Deep Learning": { "main": [[{ "node": "Merge Testing Results", "type": "main", "index": 1 }]] },
+    "Testing Agent - Symbolique": { "main": [[{ "node": "Merge Testing Results", "type": "main", "index": 2 }]] },
+    "Testing Agent - Neuro-Symbolique": { "main": [[{ "node": "Merge Testing Results", "type": "main", "index": 3 }]] },
+
+    "IF Writing Requested?": { "main": [[{ "node": "Communication Team Lead", "type": "main", "index": 0 }], []] },
+    "Communication Team Lead": { "main": [[
+      { "node": "Meeting Minutes Agent", "type": "main", "index": 0 },
+      { "node": "General Writing Agent", "type": "main", "index": 0 }
+    ]] },
+    "Meeting Minutes Agent": { "main": [[{ "node": "Merge Communication Outputs", "type": "main", "index": 0 }]] },
+    "General Writing Agent": { "main": [[{ "node": "Merge Communication Outputs", "type": "main", "index": 1 }]] },
+
+    "Article Presentation Agent": { "main": [[{ "node": "Assistant - Results Consolidation", "type": "main", "index": 0 }]] },
+    "Assistant - Results Consolidation": { "main": [[
+      { "node": "IF Send Mail?", "type": "main", "index": 0 },
+      { "node": "Final CEO Synthesis", "type": "main", "index": 0 }
+    ]] },
+    "IF Send Mail?": { "main": [[{ "node": "Send Email - Unified", "type": "main", "index": 0 }], []] },
+    "Final CEO Synthesis": { "main": [[{ "node": "Webhook Response", "type": "main", "index": 0 }]] }
+  },
+  "active": false,
+  "settings": { "executionOrder": "v1" },
+  "versionId": "3.0.0",
+  "meta": { "templateCredsSetupCompleted": true },
+  "id": "ceo-ai-enterprise-workflow-v3",
+  "tags": ["enterprise","ceo-ai","research","dblp","development","writing","multi-agent","mail-routing"]
+}