3a2567b44d
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- Migrate chat API from Ollama to LiteLLM (OpenAI-compatible SSE) - Add 15-min presenter storyline with bilingual scripts for all 20 slides - Add FAQ system (30 entries) with keyword matching for instant answers - Add IntroPresenterSlide with avatar placeholder and start button - Add PresenterOverlay (progress bar, subtitle text, play/pause/stop) - Add AvatarPlaceholder with pulse animation during speaking - Add usePresenterMode hook (state machine: idle→presenting→paused→answering→resuming) - Add 'P' keyboard shortcut to toggle presenter mode - Support [GOTO:slide-id] markers in chat responses - Dynamic slide count (was hardcoded 13, now from SLIDE_ORDER) - TTS stub prepared for future Piper integration Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
477 lines
16 KiB
TypeScript
477 lines
16 KiB
TypeScript
import { Language } from './types'
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const translations = {
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de: {
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nav: {
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slides: 'Slides',
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fullscreen: 'Vollbild',
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language: 'Sprache',
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},
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slideNames: [
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'Intro',
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'Cover',
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'Das Problem',
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'Die Loesung',
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'Produkte',
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'So funktioniert\'s',
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'Markt',
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'Geschaeftsmodell',
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'Traction',
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'Wettbewerb',
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'Team',
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'Finanzen',
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'The Ask',
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'KI Q&A',
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'Anhang: Annahmen',
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'Anhang: Architektur',
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'Anhang: Go-to-Market',
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'Anhang: Regulatorik',
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'Anhang: Engineering',
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'Anhang: KI-Pipeline',
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],
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cover: {
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tagline: 'Compliance & Code-Security fuer den Maschinenbau',
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subtitle: 'Pre-Seed · Q4 2026',
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cta: 'Pitch starten',
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},
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problem: {
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title: 'Das Problem',
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subtitle: 'Maschinenbauer entwickeln Software — aber wer sichert Compliance und Code-Sicherheit?',
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cards: [
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{
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title: 'DSGVO',
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stat: '4.1 Mrd EUR',
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desc: 'Bussgelder seit 2018. Maschinenbauer verarbeiten Kundendaten, Telemetrie und Wartungsprotokolle — oft ohne DSGVO-Prozesse.',
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},
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{
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title: 'AI Act',
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stat: 'Aug 2025',
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desc: 'Maschinen mit KI-Komponenten muessen klassifiziert werden. Embedded KI in Steuerungen und Predictive Maintenance erfordert Dokumentation.',
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},
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{
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title: 'CRA & NIS2',
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stat: '30.000+',
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desc: 'Der Cyber Resilience Act verpflichtet Hersteller, Software in ihren Produkten abzusichern. NIS2 erweitert die Cybersecurity-Pflichten auf den Maschinenbau.',
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},
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],
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quote: 'Maschinenbauer brauchen keine Compliance-Berater — sie brauchen eine KI, die ihren Code scannt, Risiken bewertet und Compliance dokumentiert.',
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},
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solution: {
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title: 'Die Loesung',
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subtitle: 'ComplAI — Compliance & Code-Security auf Autopilot',
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pillars: [
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{
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title: 'Self-Hosted Vorarbeit',
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desc: 'Mac Mini oder Mac Studio im Serverraum scannt Code, analysiert Repositories und erstellt Compliance-Dokumente. Kein Byte verlaesst das Unternehmen.',
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icon: 'server',
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},
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{
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title: 'Code-Security & DevSecOps',
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desc: 'Scannt Firmware und Software mit integrierten DevSecOps-Tools (Trivy, Semgrep, Gitleaks). Das 1000B Cloud-LLM implementiert Fixes und schreibt Risikoanalysen.',
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icon: 'scan',
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},
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{
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title: 'Compliance-KI (57 Module)',
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desc: 'Macht Ihr Unternehmen UND Ihre Produkte compliant. DSGVO, AI Act, CRA, NIS2, HinSchG — 57 SDK-Module, 19 Regularien, 2.274 indexierte Rechtstexte.',
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icon: 'bot',
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},
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],
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},
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product: {
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title: 'Unsere Produkte',
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subtitle: 'Drei Tiers fuer jede Unternehmensgroesse',
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monthly: '/Monat',
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hardware: 'Hardware',
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llm: 'KI-Modell',
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popular: 'Beliebt',
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features: 'Features',
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},
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howItWorks: {
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title: 'So funktioniert\'s',
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subtitle: 'In 4 Schritten zu Compliance & Code-Security',
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steps: [
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{
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title: 'Hardware aufstellen',
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desc: 'Mac Mini oder Mac Studio im Serverraum anschliessen. Plug & Play — scannt ab Tag 1 Ihre Repositories.',
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},
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{
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title: 'Code-Repos verbinden',
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desc: 'Git-Repos, CI/CD Pipelines und Firmware-Projekte anbinden. Die lokale KI scannt automatisch auf Schwachstellen und Compliance-Luecken.',
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},
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{
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title: 'Compliance & Security automatisieren',
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desc: 'Laufende Code-Analyse und Risikoanalysen bei jeder Aenderung. Bei kritischen Fixes schaltet sich das 1000B Cloud-LLM zu und implementiert Verbesserungen.',
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},
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{
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title: 'Audit bestehen',
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desc: 'Vollstaendige Dokumentation fuer DSGVO, AI Act, CRA und NIS2 auf Knopfdruck. Risikobeurteilungen fuer Ihre Software inklusive.',
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},
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],
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},
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market: {
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title: 'Marktchance',
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subtitle: 'Der Maschinenbau braucht Compliance & Code-Security',
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tam: 'TAM',
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sam: 'SAM',
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som: 'SOM',
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tamLabel: 'Total Addressable Market',
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samLabel: 'Serviceable Addressable Market',
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somLabel: 'Serviceable Obtainable Market',
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source: 'Quelle',
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growth: 'Wachstum p.a.',
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},
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businessModel: {
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title: 'Geschaeftsmodell',
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subtitle: 'Recurring Revenue mit Hardware-Moat',
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unitEconomics: 'Unit Economics',
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amortization: 'Amortisation',
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margin: 'Marge',
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months: 'Monate',
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recurringRevenue: 'Recurring Revenue',
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hardwareCost: 'Hardware-EK',
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operatingCost: 'Betriebskosten',
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},
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traction: {
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title: 'Traction & Meilensteine',
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subtitle: 'Unser bisheriger Fortschritt',
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completed: 'Abgeschlossen',
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inProgress: 'In Arbeit',
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planned: 'Geplant',
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},
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competition: {
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title: 'Wettbewerb',
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subtitle: '44 Features, 9 USPs — kein Anbieter kombiniert DSGVO + Code-Security + Self-Hosted KI',
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feature: 'Feature',
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selfHosted: 'Self-Hosted',
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integratedAI: 'Integrierte KI',
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autonomousSupport: 'Autonomer Support',
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yes: 'Ja',
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no: 'Nein',
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partial: 'Teilweise',
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},
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team: {
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title: 'Das Team',
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subtitle: 'Gruender mit Domain-Expertise',
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equity: 'Equity',
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expertise: 'Expertise',
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},
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financials: {
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title: 'Finanzprognose',
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subtitle: 'AI-First Kostenstruktur — skaliert ohne lineares Personalwachstum',
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revenue: 'Umsatz',
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costs: 'Kosten',
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customers: 'Kunden',
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mrr: 'MRR',
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arr: 'ARR',
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burnRate: 'Burn Rate',
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employees: 'Mitarbeiter',
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year: 'Jahr',
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sliderGrowth: 'Wachstumsrate',
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sliderChurn: 'Churn Rate',
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sliderArpu: 'ARPU',
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adjustAssumptions: 'Annahmen anpassen',
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},
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theAsk: {
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title: 'The Ask',
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subtitle: 'Pre-Seed Finanzierung',
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amount: 'Funding',
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instrument: 'Instrument',
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useOfFunds: 'Use of Funds',
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engineering: 'Engineering',
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sales: 'Vertrieb',
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hardware: 'Hardware',
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legal: 'Legal',
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reserve: 'Reserve',
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targetDate: 'Zieldatum',
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},
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aiqa: {
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title: 'Fragen? Die KI antwortet.',
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subtitle: 'Stellen Sie Ihre Investorenfragen — unser AI Agent antwortet mit Echtdaten.',
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placeholder: 'Stellen Sie eine Frage zum Investment...',
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send: 'Senden',
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thinking: 'Denke nach...',
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suggestions: [
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'Wie funktioniert die Code-Security fuer Firmware?',
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'Warum koennen Proliance und DataGuard das nicht?',
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'Was kostet die Loesung fuer einen Maschinenbauer?',
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'Wie sieht die Risikoanalyse fuer unsere Software aus?',
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],
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},
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annex: {
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assumptions: {
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title: 'Annahmen & Sensitivitaet',
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subtitle: 'Drei Szenarien fuer robuste Planung',
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},
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architecture: {
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title: 'Technische Architektur',
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subtitle: 'Self-Hosted KI-Stack fuer maximale Datensouveraenitaet',
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},
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gtm: {
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title: 'Go-to-Market Strategie',
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subtitle: 'Vom Pilot zum skalierbaren Vertrieb',
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},
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regulatory: {
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title: 'Regulatorische Details',
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subtitle: 'Die vier Saeulen der EU-Compliance fuer Maschinenbauer',
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},
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engineering: {
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title: 'Engineering Deep Dive',
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subtitle: '761K Zeilen Code \u00b7 45 Container \u00b7 100% Self-Hosted',
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},
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aipipeline: {
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title: 'KI-Pipeline Deep Dive',
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subtitle: 'RAG \u00b7 Multi-Agent-System \u00b7 Document Intelligence \u00b7 Quality Assurance',
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},
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},
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},
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en: {
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nav: {
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slides: 'Slides',
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fullscreen: 'Fullscreen',
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language: 'Language',
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},
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slideNames: [
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'Intro',
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'Cover',
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'The Problem',
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'The Solution',
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'Products',
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'How It Works',
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'Market',
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'Business Model',
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'Traction',
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'Competition',
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'Team',
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'Financials',
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'The Ask',
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'AI Q&A',
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'Appendix: Assumptions',
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'Appendix: Architecture',
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'Appendix: Go-to-Market',
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'Appendix: Regulatory',
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'Appendix: Engineering',
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'Appendix: AI Pipeline',
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],
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cover: {
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tagline: 'Compliance & Code Security for Machine Manufacturers',
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subtitle: 'Pre-Seed · Q4 2026',
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cta: 'Start Pitch',
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},
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problem: {
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title: 'The Problem',
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subtitle: 'Machine manufacturers develop software — but who ensures compliance and code security?',
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cards: [
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{
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title: 'GDPR',
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stat: 'EUR 4.1B',
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desc: 'in fines since 2018. Machine manufacturers process customer data, telemetry and maintenance logs — often without GDPR processes.',
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},
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{
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title: 'AI Act',
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stat: 'Aug 2025',
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desc: 'Machines with AI components must be classified. Embedded AI in controllers and predictive maintenance requires documentation.',
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},
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{
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title: 'CRA & NIS2',
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stat: '30,000+',
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desc: 'The Cyber Resilience Act obligates manufacturers to secure software in their products. NIS2 extends cybersecurity obligations to machine manufacturing.',
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},
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],
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quote: 'Machine manufacturers don\'t need compliance consultants — they need an AI that scans their code, assesses risks and documents compliance.',
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},
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solution: {
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title: 'The Solution',
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subtitle: 'ComplAI — Compliance & Code Security on Autopilot',
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pillars: [
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{
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title: 'Self-Hosted Preprocessing',
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desc: 'Mac Mini or Mac Studio in your server room scans code, analyzes repositories and creates compliance documents. No data leaves the company.',
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icon: 'server',
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},
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{
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title: 'Code Security & DevSecOps',
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desc: 'Scans firmware and software with integrated DevSecOps tools (Trivy, Semgrep, Gitleaks). The 1000B cloud LLM implements fixes and writes risk assessments.',
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icon: 'scan',
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},
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{
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title: 'Compliance AI (57 Modules)',
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desc: 'Makes your company AND your products compliant. GDPR, AI Act, CRA, NIS2, HinSchG — 57 SDK modules, 19 regulations, 2,274 indexed legal texts.',
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icon: 'bot',
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},
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],
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},
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product: {
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title: 'Our Products',
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subtitle: 'Three tiers for every company size',
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monthly: '/month',
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hardware: 'Hardware',
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llm: 'AI Model',
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popular: 'Popular',
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features: 'Features',
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},
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howItWorks: {
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title: 'How It Works',
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subtitle: 'Compliance & code security in 4 steps',
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steps: [
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{
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title: 'Set Up Hardware',
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desc: 'Connect Mac Mini or Mac Studio in your server room. Plug & Play — scans your repositories from day one.',
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},
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{
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title: 'Connect Code Repos',
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desc: 'Connect Git repos, CI/CD pipelines and firmware projects. The local AI automatically scans for vulnerabilities and compliance gaps.',
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},
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{
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title: 'Automate Compliance & Security',
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desc: 'Continuous code analysis and risk assessments on every change. For critical fixes, the 1000B cloud LLM steps in and implements improvements.',
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},
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{
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title: 'Pass Audits',
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desc: 'Complete documentation for GDPR, AI Act, CRA and NIS2 at the push of a button. Risk assessments for your software included.',
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},
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],
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},
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market: {
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title: 'Market Opportunity',
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subtitle: 'Machine manufacturing needs compliance & code security',
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tam: 'TAM',
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sam: 'SAM',
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som: 'SOM',
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tamLabel: 'Total Addressable Market',
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samLabel: 'Serviceable Addressable Market',
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somLabel: 'Serviceable Obtainable Market',
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source: 'Source',
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growth: 'Growth p.a.',
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},
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businessModel: {
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title: 'Business Model',
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subtitle: 'Recurring Revenue with Hardware Moat',
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unitEconomics: 'Unit Economics',
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amortization: 'Amortization',
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margin: 'Margin',
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months: 'months',
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recurringRevenue: 'Recurring Revenue',
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hardwareCost: 'Hardware Cost',
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operatingCost: 'Operating Cost',
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},
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traction: {
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title: 'Traction & Milestones',
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subtitle: 'Our progress so far',
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completed: 'Completed',
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inProgress: 'In Progress',
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planned: 'Planned',
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},
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competition: {
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title: 'Competition',
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subtitle: '44 features, 9 USPs — no provider combines GDPR + code security + self-hosted AI',
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feature: 'Feature',
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selfHosted: 'Self-Hosted',
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integratedAI: 'Integrated AI',
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autonomousSupport: 'Autonomous Support',
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yes: 'Yes',
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no: 'No',
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partial: 'Partial',
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},
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team: {
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title: 'The Team',
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subtitle: 'Founders with domain expertise',
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equity: 'Equity',
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expertise: 'Expertise',
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},
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financials: {
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title: 'Financial Projections',
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subtitle: 'AI-First cost structure — scales without linear headcount growth',
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revenue: 'Revenue',
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costs: 'Costs',
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customers: 'Customers',
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mrr: 'MRR',
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arr: 'ARR',
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burnRate: 'Burn Rate',
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employees: 'Employees',
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year: 'Year',
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sliderGrowth: 'Growth Rate',
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sliderChurn: 'Churn Rate',
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sliderArpu: 'ARPU',
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adjustAssumptions: 'Adjust Assumptions',
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},
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theAsk: {
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title: 'The Ask',
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subtitle: 'Pre-Seed Funding',
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amount: 'Funding',
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instrument: 'Instrument',
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useOfFunds: 'Use of Funds',
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engineering: 'Engineering',
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sales: 'Sales',
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hardware: 'Hardware',
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legal: 'Legal',
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reserve: 'Reserve',
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targetDate: 'Target Date',
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},
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aiqa: {
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title: 'Questions? The AI answers.',
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subtitle: 'Ask your investor questions — our AI agent responds with real data.',
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placeholder: 'Ask a question about the investment...',
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send: 'Send',
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thinking: 'Thinking...',
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suggestions: [
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'How does code security work for firmware?',
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'Why can\'t Proliance and DataGuard do this?',
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'What does the solution cost for a machine manufacturer?',
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'What does the risk assessment for our software look like?',
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],
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},
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annex: {
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assumptions: {
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title: 'Assumptions & Sensitivity',
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subtitle: 'Three scenarios for robust planning',
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},
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architecture: {
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title: 'Technical Architecture',
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subtitle: 'Self-hosted AI stack for maximum data sovereignty',
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},
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gtm: {
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title: 'Go-to-Market Strategy',
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subtitle: 'From pilot to scalable sales',
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},
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regulatory: {
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title: 'Regulatory Details',
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subtitle: 'The four pillars of EU compliance for machine manufacturers',
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},
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engineering: {
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title: 'Engineering Deep Dive',
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subtitle: '761K Lines of Code \u00b7 45 Containers \u00b7 100% Self-Hosted',
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},
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aipipeline: {
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title: 'AI Pipeline Deep Dive',
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subtitle: 'RAG \u00b7 Multi-Agent System \u00b7 Document Intelligence \u00b7 Quality Assurance',
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},
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},
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},
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}
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export function t(lang: Language): typeof translations.de {
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return translations[lang]
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}
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export function formatEur(value: number, lang: Language): string {
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if (value >= 1_000_000_000) {
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const v = (value / 1_000_000_000).toFixed(1)
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return lang === 'de' ? `${v} Mrd. EUR` : `EUR ${v}B`
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}
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if (value >= 1_000_000) {
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const v = (value / 1_000_000).toFixed(1)
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return lang === 'de' ? `${v} Mio. EUR` : `EUR ${v}M`
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}
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if (value >= 1_000) {
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const v = (value / 1_000).toFixed(0)
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return lang === 'de' ? `${v}k EUR` : `EUR ${v}k`
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}
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return lang === 'de' ? `${value} EUR` : `EUR ${value}`
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}
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export function formatNumber(value: number): string {
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return new Intl.NumberFormat('de-DE').format(value)
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}
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export default translations
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