Developed nations are building massive infrastructure projects like data centers, yet the construction workforce is aging and shrinking. This creates a critical bottleneck, as every project fundamentally relies on excavator operators—a role younger generations are avoiding.
To manage excavator blind spots, construction sites employ people to stand dangerously close and give verbal directions to the operator. This "human camera" system is a primary cause of accidents and fatalities, representing a significant, unaddressed safety and efficiency problem.
Don't wait for a prototype to get traction. Hardware founders should first engage potential customers and demonstrate a profound understanding of their specific problems. This expertise builds the necessary trust for customers to commit, even before a physical product is ready.
Unlike software, hardware iteration is slow and costly. A better approach is to resist building immediately and instead spend the majority of time on deep problem discovery. This allows you to "one-shot" a much better first version, minimizing wasted cycles on flawed prototypes.
Hardware development is often stalled by supplier lead times. To combat this, proactively map out multiple, redundant manufacturing options for every component. By maintaining a constantly updated "lookup table" of suppliers, processes, and their current lead times, teams can parallelize workflows and minimize downtime.
Competitors target easy-to-automate "drive-by-wire" excavators, which comprise only 5% of the market. Flywheel AI builds its moat by creating a solution that retrofits the other 95% of hydraulic machines. This universal compatibility is key in a price-sensitive industry with mixed fleets.
Don't shy away from competitors. A powerful customer discovery tactic is to present competing solutions directly to prospects and ask them specifically what they dislike or what's missing. This method surfaces critical product gaps and unmet needs you can build your solution around.
To achieve scalable autonomy, Flywheel AI avoids expensive, site-specific setups. Instead, they offer a valuable teleoperation service today. This service allows them to profitably collect the vast, diverse datasets required to train a generalizable autonomous system, mirroring Tesla's data collection strategy.