Enovate Motors (Tianji) \China

Step 2 - Validation (Months 7-18, $10M Series A): Expand to 3-5 OEM customers (50k+ vehicles) and launch ADAS Stack MVP. For battery AI: achieve 100k+ vehicle deployments across tier-2/tier-3 OEMs, generating $5M ARR. For ADAS: develop L2+ highway pilot (lane keeping, adaptive cruise control, automatic lane change) using pre-trained perception models (fine-tuned on 10M+ km China-specific data from partner robotaxi fleets). Deploy on NVIDIA Orin (30 TOPS, $500 hardware cost) with 8-camera setup. Pilot with 2 OEMs (5k vehicles each) at $300/vehicle/year. Demonstrate 95%+ disengagement-free highway driving and pass China's GB/T testing. Success metrics: $8M ARR ($5M battery + $3M ADAS), 80%+ gross margins, 120%+ net revenue retention. Secure 2-3 year contracts with OEMs. Key hires: 5 perception engineers, 3 planning/controls engineers, 2 sales/BD, 1 regulatory specialist. Begin fleet learning infrastructure (data pipelines processing 1TB+/day).

Step 3 - Growth (Months 19-36, $40M Series B): Scale to 15-20 OEMs (500k+ vehicles) and launch Manufacturing Copilot. For ADAS: upgrade to L3 urban driving (city streets, unprotected turns, pedestrian/cyclist handling) using transformer-based planning and reinforcement learning. Target 50k+ vehicle deployments at $400-$500/vehicle/year ($20M+ ARR). Expand internationally to India (Tata, Mahindra) and Southeast Asia (Proton, Vinfast). For Manufacturing Copilot: deploy computer vision + LLM system in 10-15 factories. Train defect detection models on 1M+ images (paint defects, panel gaps, weld quality) achieving 99.5%+ accuracy. Build generative design module using diffusion models for component optimization (reducing weight/cost). Pricing: $2M-$5M/year per factory ($30M+ pipeline). Success metrics: $50M ARR ($20M ADAS + $10M battery + $20M manufacturing), 500+ employees, profitability on unit economics (70%+ gross margins, <50% S&M as % of revenue). Establish data moat: 100M+ km fleet learning data, 10M+ manufacturing images. Key hires: 20 engineers (autonomy, CV, MLOps), 10 sales/customer success, 5 regulatory/compliance, 3 finance/ops.

Step 4 - Moat (Months 37-60, $100M+ Series C/growth equity): Achieve 50+ OEM customers (2M+ vehicles) and establish category leadership. For ADAS: launch L4 robotaxi stack for OEM partners entering autonomous ride-hailing (competing with Waymo, Cruise, Baidu Apollo). Leverage 500M+ km fleet data creating insurmountable training advantage. Pricing: $1k-$2k/vehicle for L4 capability. For Manufacturing: expand to battery cell production (partnering with CATL/BYD/LG to optimize manufacturing yield 5-10%) and semiconductor fabs (defect detection for automotive chips). Build 'Synapse Cloud'—centralized platform where OEMs share anonymized data, creating network effects (more customers → better models → more customers). Success metrics: $200M+ ARR, 75%+ gross margins, Rule of 40 compliance (growth rate + profit margin >40%), 2,000+ employees. Exit readiness: strategic acquisition by NVIDIA ($1B-$2B, integrating into DRIVE platform), Qualcomm ($800M-$1.5B, competing with Snapdragon Ride), or Bosch/Continental ($500M-$1B, adding software to hardware portfolio). Alternative: IPO at $2B-$3B valuation (10-15x ARR multiple for high-growth infrastructure software). Moat: (1) Data—1B+ km driving data, 50M+ manufacturing images creating 3-5 year training lead; (2) Integration—embedded in 50+ OEM product roadmaps with 3-5 year contracts; (3) Regulatory—approved L3/L4 stacks in China, EU, US reducing time-to-market for customers by 2-3 years; (4) Talent—200+ PhD-level researchers in perception, planning, and manufacturing AI.