Lidian \China

Step 2 - Validation: Expand to 3 ports across different geographies (US, Europe, Asia) to validate that the software generalizes across terminal layouts and equipment types. Build a simulation environment using NVIDIA Omniverse to generate 10k+ synthetic scenarios (rain, fog, night operations, equipment failures). Achieve SAE Level 4 autonomy within geofenced port areas. Hire a Head of Regulatory to secure approvals from port authorities and maritime agencies. Revenue target: $500k ARR from 100 vehicles across 3 ports.

Step 3 - Growth: Launch AutoPort Fleet OS, a SaaS platform for port operators to manage mixed fleets (autonomous + human-driven vehicles). Features include predictive maintenance, route optimization, and carbon tracking (ports face ESG pressure). Partner with terminal tractor OEMs (Kalmar, Terberg) to offer AutoPort as a factory-installed option, creating a distribution channel. Expand to adjacent use cases: rail yard shunting, warehouse yard management, and intermodal transfer. Revenue target: $10M ARR from 1000+ vehicles across 20 sites.

Step 4 - Moat: Build a proprietary dataset of port operations (millions of hours of edge cases: cranes moving overhead, forklifts crossing paths, container stacks shifting). This data becomes the moat—new entrants can't replicate it. Develop AutoPort Predict, an AI module that forecasts port congestion and optimizes container movements (reducing dwell time by 20%). License this software to ports even without autonomous vehicles, creating a second revenue stream. Explore acquisition by logistics giants (Maersk, CMA CGM) or port operators (DP World) who want to own the autonomy stack. Exit scenario: $500M-1B acquisition within 5-7 years, or continue scaling toward IPO as the dominant port autonomy provider.