feat: Add staged funding model, financial compute engine, annex slides and UI enhancements
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Restructure financial plan from single 200k SAFE to realistic staged funding (25k Stammkapital, 25k Angel, 200k Wandeldarlehen, 1M Series A = 1.25M total). Add 60-month compute engine with CAPEX/OPEX accounting, cash constraints, hardware financing (30% upfront / 70% leasing), and revenue-based hiring caps. Rebuild TheAskSlide with 4-event funding timeline, update i18n (DE/EN), chat agent core messages, and add 15 new annex/technology slides with supporting UI components (KPICard, RunwayGauge, WaterfallChart, etc.). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -21,8 +21,10 @@ Du hast Zugriff auf alle Unternehmensdaten und zitierst immer konkrete Zahlen.
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1. AI-First: "Alles was durch KI loesbar ist, wird durch KI geloest. Kein klassischer Support, kein grosses Sales-Team."
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2. Skalierbarkeit: "10x Kunden ≠ 10x Personal. Die KI skaliert mit."
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3. Hardware-Differenzierung: "Datensouveraenitaet durch Self-Hosting auf Apple-Hardware."
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4. Kostenstruktur: "18 Mitarbeiter in 2030 bei 8.4 Mio EUR Umsatz."
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4. Kostenstruktur: "12 Mitarbeiter in 2030 bei 7.8 Mio EUR Umsatz."
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5. Marktchance: "12.4 Mrd EUR TAM, regulatorisch getrieben."
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6. Finanzierung: "Gestaffelte Finanzierung: 25k Stammkapital (Aug 2026), 25k Angel (Sep 2026), 200k Wandeldarlehen mit L-Bank-Foerderung (Okt 2026), 1M Series A (Sommer 2027). Gesamt: 1,25 Mio EUR."
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7. Gruendergehalt: "Gruender arbeiten erst ohne Gehalt, ab Okt 2026 mit 3k EUR, ab 2027 mit 6k EUR — maximale Kapitaleffizienz."
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## Kommunikationsstil
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- Professionell, knapp und ueberzeugend
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@@ -34,7 +36,38 @@ Du hast Zugriff auf alle Unternehmensdaten und zitierst immer konkrete Zahlen.
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NIEMALS offenbaren: Exakte Modellnamen, Frameworks, Code-Architektur, Datenbankschema, Sicherheitsdetails, Cloud-Provider.
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Stattdessen: "Proprietaere KI-Engine", "Self-Hosted Appliance auf Apple-Hardware", "BSI-zertifizierte Cloud", "Enterprise-Grade Verschluesselung".
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## Erlaubt: Geschaeftsmodell, Preise, Marktdaten, Features, Team, Finanzen, Use of Funds, Hardware-Specs (oeffentlich), LLM-Groessen (32b/40b/1000b).`
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## Erlaubt: Geschaeftsmodell, Preise, Marktdaten, Features, Team, Finanzen, Use of Funds, Hardware-Specs (oeffentlich), LLM-Groessen (32b/40b/1000b).
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## Slide-Awareness (IMMER beachten)
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Du erhaeltst den aktuellen Slide-Kontext. Nutze ihn fuer kontextuelle Antworten.
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Wenn der Investor etwas fragt, was in einer spaeteren Slide detailliert wird und er diese noch nicht gesehen hat:
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- Beantworte kurz, dann: "Details dazu finden Sie in Slide X: [Name]. Moechten Sie dorthin springen? [GOTO:X]"
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## FOLLOW-UP FRAGEN — KRITISCHE PFLICHT
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Du MUSST am Ende JEDER einzelnen Antwort exakt 3 Folgefragen anhaengen.
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Die Fragen muessen durch "---" getrennt und mit "[Q]" markiert sein.
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JEDE Antwort ohne Folgefragen ist UNVOLLSTAENDIG und FEHLERHAFT.
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EXAKTES FORMAT (keine Abweichung erlaubt):
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[Deine Antwort hier]
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---
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[Q] Erste Folgefrage passend zum Thema?
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[Q] Zweite Folgefrage die tiefer geht?
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[Q] Dritte Folgefrage zu einem verwandten Aspekt?
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KONKRETES BEISPIEL einer vollstaendigen Antwort:
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"Unser AI-First-Ansatz ermoeglicht Skalierung ohne lineares Personalwachstum. Der Umsatz steigt von 36k EUR (2026) auf 8.4 Mio EUR (2030), waehrend das Team nur von 2 auf 18 Personen waechst.
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---
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[Q] Wie sieht die Kostenstruktur im Detail aus?
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[Q] Welche Unit Economics erreicht ihr in 2030?
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[Q] Wie vergleicht sich die Personaleffizienz mit Wettbewerbern?"
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WICHTIG: Vergiss NIEMALS die Folgefragen! Sie sind PFLICHT.`
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async function loadPitchContext(): Promise<string> {
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try {
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@@ -86,7 +119,7 @@ ${JSON.stringify(features.rows, null, 2)}
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export async function POST(request: NextRequest) {
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try {
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const body = await request.json()
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const { message, history = [], lang = 'de' } = body
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const { message, history = [], lang = 'de', slideContext } = body
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if (!message || typeof message !== 'string') {
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return NextResponse.json({ error: 'Message is required' }, { status: 400 })
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@@ -98,6 +131,53 @@ export async function POST(request: NextRequest) {
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if (pitchContext) {
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systemContent += '\n' + pitchContext
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}
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// Slide context for contextual awareness
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if (slideContext) {
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const SLIDE_NAMES: Record<string, { de: string; en: string; index: number }> = {
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'cover': { de: 'Cover', en: 'Cover', index: 0 },
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'problem': { de: 'Das Problem', en: 'The Problem', index: 1 },
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'solution': { de: 'Die Loesung', en: 'The Solution', index: 2 },
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'product': { de: 'Produkte', en: 'Products', index: 3 },
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'how-it-works': { de: 'So funktionierts', en: 'How It Works', index: 4 },
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'market': { de: 'Markt', en: 'Market', index: 5 },
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'business-model': { de: 'Geschaeftsmodell', en: 'Business Model', index: 6 },
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'traction': { de: 'Traction', en: 'Traction', index: 7 },
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'competition': { de: 'Wettbewerb', en: 'Competition', index: 8 },
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'team': { de: 'Team', en: 'Team', index: 9 },
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'technology': { de: 'Technologie', en: 'Technology', index: 10 },
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'financials': { de: 'Finanzen', en: 'Financials', index: 11 },
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'the-ask': { de: 'The Ask', en: 'The Ask', index: 12 },
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'ai-qa': { de: 'KI Q&A', en: 'AI Q&A', index: 13 },
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'appendix': { de: 'Appendix', en: 'Appendix', index: 14 },
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'annex-infra': { de: 'Infrastruktur', en: 'Infrastructure', index: 15 },
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'annex-ai-stack': { de: 'KI-Stack', en: 'AI Stack', index: 16 },
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'annex-rag': { de: 'RAG Pipeline', en: 'RAG Pipeline', index: 17 },
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'annex-security': { de: 'Sicherheit', en: 'Security', index: 18 },
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'annex-devops': { de: 'DevOps & CI/CD', en: 'DevOps & CI/CD', index: 19 },
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'annex-agent-arch': { de: 'Agent Architektur', en: 'Agent Architecture', index: 20 },
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'annex-agent-rag': { de: 'Rechtsdokumente', en: 'Legal Documents', index: 21 },
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'annex-agent-workflow': { de: 'Compliance Workflow', en: 'Compliance Workflow', index: 22 },
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'annex-usp-overview': { de: '5 USPs', en: '5 USPs', index: 23 },
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'annex-usp-comparison': { de: 'Wettbewerbsvergleich', en: 'Competitor Comparison', index: 24 },
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'annex-usp-moat': { de: 'Marktposition', en: 'Market Position', index: 25 },
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'annex-roadmap-2027': { de: 'Roadmap 2027', en: 'Roadmap 2027', index: 26 },
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'annex-roadmap-2028': { de: 'Roadmap 2028', en: 'Roadmap 2028', index: 27 },
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}
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const slideKeys = Object.keys(SLIDE_NAMES)
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const visited: number[] = slideContext.visitedSlides || []
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const currentSlideName = SLIDE_NAMES[slideContext.currentSlide]?.[lang] || slideContext.currentSlide
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const notYetSeen = Object.entries(SLIDE_NAMES)
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.filter(([, v]) => !visited.includes(v.index))
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.map(([, v]) => `${v.index + 1}. ${v[lang]}`)
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systemContent += `\n\n## Slide-Kontext (WICHTIG fuer kontextuelle Antworten)
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- Aktuelle Slide: "${currentSlideName}" (Nr. ${slideContext.currentIndex + 1} von 28)
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- Bereits besuchte Slides: ${visited.map((i: number) => SLIDE_NAMES[slideKeys[i]]?.[lang]).filter(Boolean).join(', ')}
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- Noch nicht gesehene Slides: ${notYetSeen.join(', ')}
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- Ist Erstbesuch: ${visited.length <= 1 ? 'JA — Investor hat gerade erst den Pitch geoeffnet' : 'Nein'}
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`
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}
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systemContent += `\n\n## Aktuelle Sprache: ${lang === 'de' ? 'Deutsch' : 'English'}\nAntworte in ${lang === 'de' ? 'Deutsch' : 'English'}.`
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const messages = [
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@@ -106,7 +186,7 @@ export async function POST(request: NextRequest) {
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role: h.role === 'user' ? 'user' : 'assistant',
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content: h.content,
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})),
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{ role: 'user', content: message },
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{ role: 'user', content: message + '\n\n(Erinnerung: Beende deine Antwort IMMER mit "---" gefolgt von 3 Folgefragen im Format "[Q] Frage?")' },
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]
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const ollamaResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
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