The Situation
The client had proven the technology. The harder question was whether it could become a business: how do you scale autonomous ride-hail commercially without assuming the capital burden of owning a fleet?
I built the business value case, a financial model and partnership framework answering whether an asset-light model could work, what the economics looked like for both the company and its partners, and which path to market made sense. Every figure produced was a modeled scenario, built where no operating precedent existed.
Challenge
Can the economics work?
At a modeled 2,500 vehicles, are unit economics strong enough to attract fleet operators and capital without the company owning the assets?
Who owns what?
A franchise model with partner-owned fleets versus a corporate model with operator-owned fleets, each carrying a different risk, return, and scalability profile.
What makes a partner say yes?
Fleet operators, rental companies, PE firms, and OEM dealerships each had different motivations. The case had to prove partner returns, not just client returns.
Trust as the gating factor
The client's equity as a technology company was strong, but its equity as a ride-hail provider was nascent, and consumer adoption drove the unit economics.
What drives returns?
Charging costs were modeled at roughly 80% of OpEx, making energy infrastructure the primary lever on partner economics rather than fleet size. This was a modeling finding, not an observed operating result.
Regulatory surface
AV regulation was evolving at federal and local levels simultaneously, so any commercial model had to hold under constraints that would keep shifting.
Actions
First-Principles Financial Model
Two revenue-split scenarios modeled (20/80 and 70/30 between client and fleet operator), projecting NOI for both parties and isolating the variables that moved partner returns most.
Partnership Framework
Franchise and corporate structures evaluated across fleet operators, rental companies, PE firms, OEM dealerships, robo-taxi fleets, and consumer brands.
Recommendation: Franchise-First
Lower client CapEx, faster market entry, and consumer brand partnerships as the adoption accelerant, delivered to program leadership.
Results
Up to $163M partner NOI modeled
Modeled projectionUnder the asset-light split, partner economics modeled strongly enough to attract capital without the client subsidizing the deal. A projection, not a realized outcome.
$61M client NOI modeled
Modeled projectionAt the asset-light split the client retains the technology premium while offloading capital risk, and still projects $61M NOI at the modeled initial scale.
Fleet operator returns modeled across fleet sizes
Modeled projectionModeled returns hold across fleet sizes because ride volume, not scale, drives them. A 500-vehicle operator still models a viable business case.
Corporate model returns modeled
Modeled projectionEven absorbing full OpEx including charging, modeled client returns leave headroom to subsidize partner adoption.
Charging identified as the primary OpEx lever
Modeling findingEnergy cost management modeled as the single largest lever on partner economics. An analytical finding from the model, not an observed operating result.
Path-to-market recommendation delivered
Realized resultLower CapEx, faster entry, and existing fleet operators as the most viable first partners, with consumer brand partnerships as the trust accelerant.
All financial figures on this page are modeled projections under assumptions agreed with client leadership. They are not achieved results.
She was able to articulately speak to each lever and bring it to a higher level executive narrative.