Arrinera \Poland

Step 2 - Validation (Months 7-12, $500K): Expand to chassis design optimization and crash simulation for Tier-1 suppliers. Target: 5 suppliers (Brembo, ZF, Tenneco) paying $100K-200K/year for multi-user licenses. Deliver: Add generative design for suspension components, integrate Ansys LS-DYNA for crash testing, build collaboration features (design review, version control, approval workflows). Validation metric: $750K ARR, 90%+ gross margins, <6 month sales cycles. Hire: 2 automotive engineers (ex-Bosch/Continental), 1 ML engineer, 1 sales exec with Tier-1 relationships. Prove that AI-generated designs pass real-world validation (physical prototypes match simulation predictions within 5%).

Step 3 - Growth (Months 13-24, $3M): Launch full vehicle development platform for OEMs and scale to $5M ARR. Target: 2 OEM pilot programs (Stellantis, Renault) at $500K-1M/year, 20 Tier-1 suppliers at $150K/year average. Deliver: End-to-end workflow from requirements (natural language input: 'design a 5-seat SUV with 300-mile range under $40K BOM') to manufacturable CAD models, BOM generation, supply chain cost optimization, regulatory compliance checking (automated FMVSS/ECE crash test prediction). Validation metric: $5M ARR, 120% net dollar retention, 1 OEM design goes to production using our platform (case study for sales). Hire: 10 engineers, 3 sales, 2 customer success, 1 automotive regulatory expert. Build moat through proprietary dataset (10,000+ vehicle designs, 50,000+ crash simulations) that makes our AI models 10x more accurate than competitors.

Step 4 - Moat (Months 25-36, $10M): Build network effects through supply chain integration and become the 'operating system' for automotive development. Target: $20M ARR, 50+ enterprise customers, 500+ suppliers in marketplace. Deliver: (1) Supply chain marketplace where OEMs/Tier-1s can instantly source quotes for components designed in our platform (take 3-5% transaction fee), (2) AI agent that autonomously optimizes designs for cost/performance trade-offs by querying real-time supplier pricing, (3) Regulatory compliance-as-a-service (automated homologation documentation generation, crash test prediction with 95%+ accuracy certified by TÜV/SGS), (4) Open API for third-party integrations (PLM systems like Siemens Teamcenter, ERP systems like SAP). Moat: (A) Data network effects—every design iteration improves AI models, making platform more valuable; (B) Switching costs—once OEM builds design library in our platform, migration is prohibitively expensive; (C) Supply chain lock-in—suppliers join to access OEM buyers, OEMs stay for supplier access. Exit strategy: Acquisition by Siemens ($50-100M for product line integration), Dassault Systèmes, Autodesk, or Ansys; OR continue scaling to $100M ARR and IPO as vertical SaaS (comparable to Altair Engineering, $500M revenue, $3B market cap).