[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_164":165,"_166":167},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":-7,"_18":19,"_17":-7,"_124":-5,"_20":125,"_22":126,"_127":128,"_129":130,"_26":131,"_24":132,"_133":-5,"_134":-5,"_135":51,"_136":137,"_138":43,"_139":-5,"_140":-5,"_141":142},"routes/_app.x.$handle.$name.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":-5,"_29":-5,"_30":-5,"_31":-5,"_32":-5},"handle","CubinkDigital","name","scan-referent-ia","branchName","main","version","path","scan-template.html","project",{"_33":70,"_13":14,"_44":71,"_72":-5,"_73":51,"_74":75,"_76":75,"_77":78,"_79":-5,"_80":81,"_82":62,"_83":43,"_84":-5,"_85":-5,"_86":65,"_87":88,"_89":-5,"_90":-5,"_91":-5,"_92":-5,"_93":94,"_95":94,"_96":97,"_98":97,"_99":97,"_100":94,"_101":94,"_102":103,"_104":105,"_106":107,"_108":51},"tree",[54,55],"file",{"_33":34,"_13":19,"_35":36,"_37":-5,"_38":39,"_40":41,"_42":43,"_44":45,"_17":46,"_47":24,"_48":49,"_50":51,"_52":-5,"_18":53},"contents",[],"upstream","pull","ahead","behind","envVars","id","63e7e288-7ec8-4d8a-97f1-b687efdfb3a9","fileId","9d05a862-81d8-11f1-a01a-1607ee4eb77e","parentId","branchId","31eafb46-53b1-4c37-bb29-61bf7d5cdc94","branchVersion",7,"latest",true,"createdAt",["D",1784314670097],5,"fileType","content","\n\n\n\n\n\nScan IA — {{CLIENT_NAME}} | Référent IA\n\n\n\n\n\n\n

\n \n \n \n

\n 🔗 {{CLIENT_URL}}\n · {{CLIENT_LOCATION}}\n

Rapport du {{REPORT_DATE}} · Analyse externe publique · {{REPORT_VERSION}}

\n\n\n

\n\n\n \n

Score Facteur Confiance

\n \n

\n\n \n

Vos scores en détail

\n\n \n

📋

Synthèse

\n {{VERDICT_BODY}}\n

\n\n\n \n

\n \n

Cohérence

\n \n

Visibilité IA

\n \n

Infrastructure revenus

\n \n

Facteur Confiance

\n\n \n

Comment lire ce rapport

\n Ceci est une analyse externe complète de ce que vos clients — et les outils d'IA qu'ils utilisent de plus en plus pour trouver une entreprise — voient réellement quand ils vous cherchent. Tout repose sur des informations publiquement accessibles uniquement. Aucune donnée interne n'a été consultée.\n

\n Un cabinet de conseil facturerait plusieurs milliers d'euros pour une analyse de ce type. Vous la recevez gratuitement parce que nous pensons que le diagnostic doit venir avant la conversation.\n

Étape 1

Visibilité IA

ChatGPT, Gemini et les autres IA trouvent-elles votre entreprise ? La recommandent-elles ?

Étape 2

Cohérence + confiance

Votre entreprise raconte-t-elle une histoire cohérente ? Inspire-t-elle confiance en ligne ?

Étape 3

Opportunités IA + plan d'action

Les heures récupérables chaque semaine avec les bons outils — et par quoi commencer.

\n\n \n

\n \n

\n {{REVENUE_INTRO}}\n

\n {{REVENUE_ITEMS}}\n

\n\n \n

\n \n

\n {{ISSUES_INTRO}}\n

\n\n \n

🔧 Solution : {{ISSUE_1_FIX}}

\n\n \n

🔧 Solution : {{ISSUE_2_FIX}}

\n\n \n

🔧 Solution : {{ISSUE_3_FIX}}

\n\n \n

🔧 Solution : {{ISSUE_4_FIX}}

\n\n \n

🔧 Solution : {{ISSUE_5_FIX}}

\n\n \n

\n \n

\n {{AI_INTRO}}\n

\n\n \n

Profil de visibilité IA — 9 dimensions (échelle 0–100)

\n\n \n

Cohérence

Visibilité IA

Revenus

Global

↓ Le détail complet dans chaque section ci-dessous

\n\n \n

Ce que les IA ont répondu lors de notre test — {{REPORT_DATE}}

PLATEFORME

TROUVE-T-ELLE {{CLIENT_NAME}} ?

RECOMMANDATION

ChatGPT

Claude

Google Gemini

Perplexity

Grok

Gemini Deep Research

Genspark

\n\n

📍 L'essentiel

\n {{AI_BOTTOMLINE}}\n

\n\n \n

\n \n

\n {{CUBE_INTRO}}\n

Clarté de l'identité de marque

Profondeur des pages services

Couverture géographique

Données structurées (Schema)

Profils de l'équipe

Coordonnées de contact

FAQ et contenu conversationnel

Signaux de confiance

Capture de contacts hors horaires

Qualité des fiches (Google, annuaires)

Publication de contenu

Preuve sociale (avis clients)

Présence sur les réseaux sociaux

Performance technique du site

Qualité des appels à l'action

\n 🧊 Score Cube : {{CUBE_SCORE_VALUE}}/100 — {{CUBE_SCORE_FAILS}} contrôles sur 15 en échec, {{CUBE_SCORE_PARTIAL}} partiels. {{CUBE_SCORE_SUMMARY}}\n

\n\n \n

\n \n

\n ⚠️ Alerte comparaison
\n Panel de comparaison : {{COMP_BENCHMARK}}. Voici la comparaison honnête sur les six dimensions qui comptent le plus en 2026 — et où {{CLIENT_NAME}} perd du terrain aujourd'hui :\n

✅ Top 1 % : {{COMP_1_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_1_CURRENT}}

✅ Top 1 % : {{COMP_2_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_2_CURRENT}}

✅ Top 1 % : {{COMP_3_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_3_CURRENT}}

✅ Top 1 % : {{COMP_4_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_4_CURRENT}}

✅ Top 1 % : {{COMP_5_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_5_CURRENT}}

✅ Top 1 % : {{COMP_6_TOP}}

⚠️ {{CLIENT_NAME}} aujourd'hui : {{COMP_6_CURRENT}}

\n\n \n

\n \n

\n {{OPP_INTRO}}\n

{{OPP_TOTAL_HOURS}} h

récupérables par semaine, au total, sur la base de ce que nous voyons de l'extérieur. Le Diagnostic affine ce chiffre avec vos temps réels, activité par activité.

📍 L'essentiel

\n {{OPP_BOTTOMLINE}}\n

\n\n \n

\n \n

📅 Cette semaine

\n {{PHASE_1_ITEMS}}\n

📅 Jours 7 à 14

\n {{PHASE_2_ITEMS}}\n

📅 Jours 14 à 30

\n {{PHASE_3_ITEMS}}\n

\n\n \n \n

P R O C H A I N E É T A P E

\n Prêt à voir
comment corriger tout ça ?\n

\n Le Diagnostic complet va plus loin : un entretien de 45 minutes sur vos activités réelles, un rapport chiffré outil par outil, et un plan de démarrage en 4 jours.
Payé au résultat : si nous n'identifions pas 5 heures par semaine récupérables, vous êtes remboursé.\n

\n \n Lancer mon Diagnostic (997 € nets) →\n \n

Diagnostic : 997 € nets. Six entreprises accompagnées à la fois.

Vous connaissez un autre dirigeant qui devrait voir son Scan ?

\n Offrez-lui son propre Scan →\n

\n\n \n\n\n\n\n","deleted",false,"label",[19],{"_33":60,"_13":61,"_35":62,"_37":-5,"_38":39,"_40":63,"_42":43,"_44":64,"_17":65,"_47":66,"_48":67,"_50":51,"_52":-5,"_57":68,"_18":69},{"_33":34,"_13":19,"_35":36,"_37":-5,"_38":39,"_40":41,"_42":43,"_44":56,"_17":46,"_47":24,"_48":49,"_50":51,"_52":-5,"_57":58,"_18":59},["D",1784314670097],"children",[],[19],"2534d675-c45e-499d-ac93-beedf49ae8b9","main.ts","c277b9c8-81d8-11f1-a3c4-1607ee4eb77e",2,["D",1784290340059],0,"http","// VERSION: Scan Référent IA V1.0 — adapté de Trust Radar V2.0 (ai-radar-ON, intouché)\nimport { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { readFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nawait sqlite.execute(\n `CREATE TABLE IF NOT EXISTS scan_reports (id TEXT PRIMARY KEY, html TEXT NOT NULL, client_name TEXT, client_email TEXT, overall_score INTEGER, created_at TEXT)`,\n);\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n if (req.method === \"GET\" && url.searchParams.get(\"id\")) {\n const id = url.searchParams.get(\"id\")!;\n const result = await sqlite.execute(\n `SELECT html FROM scan_reports WHERE id = ?`,\n [id],\n );\n if (!result.rows.length) {\n return new Response(\"

Rapport introuvable

\", {\n status: 404,\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n }\n return new Response(result.rows[0][0] as string, {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n }\n if (req.method !== \"POST\") {\n return Response.json({ error: \"POST only\", version: \"Scan Référent IA V1.0\" }, {\n status: 405,\n });\n }\n const secret = req.headers.get(\"x-radar-secret\");\n if (secret !== Deno.env.get(\"RADAR_SECRET\")) {\n return Response.json({ error: \"Unauthorized\" }, { status: 401 });\n }\n let body: any;\n try {\n body = await req.json();\n } catch {\n return Response.json({ error: \"Invalid JSON\" }, { status: 400 });\n }\n let data: any;\n try {\n let c = typeof body.apiJson === \"string\"\n ? body.apiJson\n : JSON.stringify(body.apiJson);\n c = c.trim().replace(/^```json\\s*/i, \"\").replace(/^```\\s*/i, \"\").replace(\n /```\\s*$/i,\n \"\",\n ).trim();\n const s = c.indexOf(\"{\"), e = c.lastIndexOf(\"}\");\n if (s >= 0 && e > s) c = c.slice(s, e + 1);\n try {\n data = JSON.parse(c);\n } catch {\n // La sortie LLM n'est pas toujours du JSON strict (virgules finales, etc.) — réparer avant de rejeter\n const { jsonrepair } = await import(\"npm:jsonrepair@3.13.1\");\n data = JSON.parse(jsonrepair(c));\n }\n } catch (e: any) {\n return Response.json({ error: \"Parse error: \" + e.message }, {\n status: 400,\n });\n }\n\n let html = await readFile(\"/scan-template.html\", import.meta.url);\n\n function safe(v: any) {\n return v != null ? String(v) : \"\";\n }\n function r(t: string, v: any) {\n html = html.split(\"{{\" + t + \"}}\").join(safe(v));\n }\n function sc(v: number) {\n return v >= 70 ? \"#27ae60\" : v >= 45 ? \"#e67e22\" : \"#c0392b\";\n }\n function ss(v: number) {\n return v >= 70 ? \"Solide\" : v >= 45 ? \"À travailler\" : \"Critique\";\n }\n function sd(v: number) {\n return Math.round((v / 100) * 157.08 * 10) / 10;\n }\n function today() {\n return new Date().toLocaleDateString(\"fr-FR\", {\n year: \"numeric\",\n month: \"long\",\n day: \"numeric\",\n });\n }\n\n const scores: any = {\n COHERENCE: parseInt(data.scores?.SCORE_COHERENCE_VALUE) || 0,\n VISIBILITY: parseInt(data.scores?.SCORE_VISIBILITY_VALUE) || 0,\n REVENUE: parseInt(data.scores?.SCORE_REVENUE_VALUE) || 0,\n };\n\n r(\"CLIENT_NAME\", data.client?.CLIENT_NAME);\n r(\"CLIENT_URL\", data.client?.CLIENT_URL);\n r(\"CLIENT_LOCATION\", data.client?.CLIENT_LOCATION);\n r(\"REPORT_DATE\", today());\n r(\"REPORT_VERSION\", \"Scan Référent IA V1.0\");\n r(\"BUSINESS_MODEL\", data.client?.BUSINESS_MODEL);\n\n for (const [k, v] of Object.entries(scores)) {\n const n = v as number;\n r(\"SCORE_\" + k + \"_VALUE\", n);\n r(\"SCORE_\" + k + \"_DASH\", sd(n));\n r(\"SCORE_\" + k + \"_COLOR\", sc(n));\n r(\"SCORE_\" + k + \"_STATUS\", ss(n));\n }\n\n // Facteur Confiance — calculé localement (pondération PME : 30/30/40)\n const tf_value = Math.round(\n scores.COHERENCE * 0.30 + scores.VISIBILITY * 0.30 + scores.REVENUE * 0.40,\n );\n\n let tf_label: string;\n if (tf_value >= 86) tf_label = \"Leader de confiance\";\n else if (tf_value >= 66) tf_label = \"Confiance établie\";\n else if (tf_value >= 41) tf_label = \"Confiance en construction\";\n else tf_label = \"Confiance à bâtir\";\n\n let tf_color: string;\n if (tf_value >= 86) tf_color = \"#edca69\";\n else if (tf_value >= 66) tf_color = \"#27ae60\";\n else if (tf_value >= 41) tf_color = \"#e67e22\";\n else tf_color = \"#c0392b\";\n\n const tf_dash = Math.round((tf_value / 100) * 157.08 * 100) / 100;\n\n let worst: string;\n if (\n scores.REVENUE <= scores.COHERENCE && scores.REVENUE <= scores.VISIBILITY\n ) worst = \"l'infrastructure de revenus\";\n else if (scores.VISIBILITY <= scores.COHERENCE) worst = \"la visibilité IA\";\n else worst = \"la cohérence de l'entreprise\";\n\n const tf_from_claude = data.trust_factor\n ? data.trust_factor.TRUST_FACTOR_BODY\n : null;\n const tf_body = tf_from_claude ||\n (\"Cette entreprise obtient \" + tf_value +\n \"/100 au Facteur Confiance. La dimension qui tire le score vers le bas est \" +\n worst +\n \". C'est le levier le plus rapide pour faire bouger ce chiffre.\");\n\n r(\"TRUST_FACTOR_VALUE\", tf_value);\n r(\"TRUST_FACTOR_LABEL\", tf_label);\n r(\"TRUST_FACTOR_COLOR\", tf_color);\n r(\"TRUST_FACTOR_DASH\", tf_dash);\n r(\"TRUST_FACTOR_BODY\", tf_body);\n\n r(\"SCORE_OVERALL_VALUE\", tf_value);\n r(\"SCORE_OVERALL_DASH\", tf_dash);\n r(\"SCORE_OVERALL_COLOR\", tf_color);\n r(\"SCORE_OVERALL_STATUS\", tf_label);\n\n // Verdict\n const verdictData = data.verdict || {};\n r(\n \"VERDICT_ICON\",\n verdictData.VERDICT_ICON ||\n (tf_value >= 66 ? \"✅\" : tf_value >= 41 ? \"⚠️\" : \"🔴\"),\n );\n r(\"VERDICT_TITLE\", verdictData.VERDICT_TITLE || tf_label);\n r(\"VERDICT_BODY\", verdictData.VERDICT_BODY || \"\");\n\n // Visibilité IA — jetons numérotés du template\n r(\"AI_INTRO\", data.ai_visibility?.AI_INTRO);\n r(\"AI_RADAR_DATA\", JSON.stringify(data.ai_visibility?.AI_RADAR_DATA));\n r(\"AI_BOTTOMLINE\", data.ai_visibility?.AI_BOTTOMLINE);\n for (let i = 1; i <= 7; i++) {\n r(\"AI_P\" + i + \"_FOUND\", data.ai_visibility?.[\"AI_P\" + i + \"_FOUND\"]);\n r(\n \"AI_P\" + i + \"_FOUND_COLOR\",\n data.ai_visibility?.[\"AI_P\" + i + \"_FOUND_COLOR\"],\n );\n r(\"AI_P\" + i + \"_REC\", data.ai_visibility?.[\"AI_P\" + i + \"_REC\"]);\n r(\n \"AI_P\" + i + \"_REC_COLOR\",\n data.ai_visibility?.[\"AI_P\" + i + \"_REC_COLOR\"],\n );\n }\n\n // CUBE — jetons numérotés du template\n r(\"CUBE_INTRO\", data.cube?.CUBE_INTRO);\n r(\"CUBE_SCORE_VALUE\", data.cube?.CUBE_SCORE_VALUE);\n r(\"CUBE_SCORE_FAILS\", data.cube?.CUBE_SCORE_FAILS);\n r(\"CUBE_SCORE_PARTIAL\", data.cube?.CUBE_SCORE_PARTIAL);\n r(\"CUBE_SCORE_SUMMARY\", data.cube?.CUBE_SCORE_SUMMARY);\n for (let i = 1; i <= 15; i++) {\n r(\"CUBE_C\" + i + \"_STATUS\", data.cube?.[\"CUBE_C\" + i + \"_STATUS\"]);\n r(\"CUBE_C\" + i + \"_EMOJI\", data.cube?.[\"CUBE_C\" + i + \"_EMOJI\"]);\n r(\"CUBE_C\" + i + \"_NOTE\", data.cube?.[\"CUBE_C\" + i + \"_NOTE\"]);\n }\n\n // Problèmes — jetons numérotés du template\n r(\"ISSUES_INTRO\", data.issues?.ISSUES_INTRO);\n for (let i = 1; i <= 5; i++) {\n r(\"ISSUE_\" + i + \"_SEVERITY\", data.issues?.[\"ISSUE_\" + i + \"_SEVERITY\"]);\n r(\"ISSUE_\" + i + \"_BADGE\", data.issues?.[\"ISSUE_\" + i + \"_BADGE\"]);\n r(\"ISSUE_\" + i + \"_COLOR\", data.issues?.[\"ISSUE_\" + i + \"_COLOR\"]);\n r(\"ISSUE_\" + i + \"_TITLE\", data.issues?.[\"ISSUE_\" + i + \"_TITLE\"]);\n r(\"ISSUE_\" + i + \"_BODY\", data.issues?.[\"ISSUE_\" + i + \"_BODY\"]);\n r(\"ISSUE_\" + i + \"_FIX\", data.issues?.[\"ISSUE_\" + i + \"_FIX\"]);\n }\n\n // Revenus — jetons numérotés du template\n r(\"REVENUE_INTRO\", data.revenue?.REVENUE_INTRO);\n r(\"REVENUE_BAR_LABELS\", JSON.stringify(data.revenue?.REVENUE_BAR_LABELS));\n r(\"REVENUE_BAR_DATA\", JSON.stringify(data.revenue?.REVENUE_BAR_DATA));\n r(\"REVENUE_BAR_COLORS\", JSON.stringify(data.revenue?.REVENUE_BAR_COLORS));\n r(\"REVENUE_ITEMS\", data.revenue?.REVENUE_ITEMS);\n\n // Concurrents — jetons numérotés du template\n r(\"COMP_BENCHMARK\", data.competitors?.COMP_BENCHMARK);\n for (let i = 1; i <= 6; i++) {\n r(\"COMP_\" + i + \"_DIM\", data.competitors?.[\"COMP_\" + i + \"_DIM\"]);\n r(\n \"COMP_\" + i + \"_BADGE_COLOR\",\n data.competitors?.[\"COMP_\" + i + \"_BADGE_COLOR\"],\n );\n r(\n \"COMP_\" + i + \"_BADGE_TEXT\",\n data.competitors?.[\"COMP_\" + i + \"_BADGE_TEXT\"],\n );\n r(\"COMP_\" + i + \"_TOP\", data.competitors?.[\"COMP_\" + i + \"_TOP\"]);\n r(\"COMP_\" + i + \"_CURRENT\", data.competitors?.[\"COMP_\" + i + \"_CURRENT\"]);\n }\n\n // Opportunités IA — nouvelle section (outils + heures récupérables)\n r(\"OPP_INTRO\", data.opportunites?.OPP_INTRO);\n for (let i = 1; i <= 5; i++) {\n r(\"OPP_\" + i + \"_TOOL\", data.opportunites?.[\"OPP_\" + i + \"_TOOL\"]);\n r(\"OPP_\" + i + \"_USE\", data.opportunites?.[\"OPP_\" + i + \"_USE\"]);\n r(\"OPP_\" + i + \"_HOURS\", data.opportunites?.[\"OPP_\" + i + \"_HOURS\"]);\n r(\"OPP_\" + i + \"_COST\", data.opportunites?.[\"OPP_\" + i + \"_COST\"]);\n }\n r(\"OPP_TOTAL_HOURS\", data.opportunites?.OPP_TOTAL_HOURS);\n r(\"OPP_BOTTOMLINE\", data.opportunites?.OPP_BOTTOMLINE);\n\n // Plan d'action\n r(\"PHASE_1_ITEMS\", data.action_plan?.PHASE_1_ITEMS);\n r(\"PHASE_2_ITEMS\", data.action_plan?.PHASE_2_ITEMS);\n r(\"PHASE_3_ITEMS\", data.action_plan?.PHASE_3_ITEMS);\n\n html = html.replace(/\\{\\{[A-Z_0-9]+\\}\\}/g, (m) => \"[MANQUANT:\" + m + \"]\");\n\n const id = crypto.randomUUID();\n const overall = tf_value;\n\n await sqlite.execute(\n `INSERT INTO scan_reports (id, html, client_name, client_email, overall_score, created_at) VALUES (?, ?, ?, ?, ?, ?)`,\n [\n id,\n html,\n data.client?.CLIENT_NAME || \"\",\n data.client?.CLIENT_EMAIL || \"\",\n overall,\n new Date().toISOString(),\n ],\n );\n\n const reportUrl = url.origin + \"?id=\" + id;\n return Response.json({\n success: true,\n reportUrl,\n id,\n overall_score: overall,\n });\n}\n",[],[61],"b69cefbd-a737-4529-a12d-7d3782a37a01",["D",1784290245505],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Scan IA Référent IA — génère le rapport français (Make → 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