CoPilot

The problem

In car-dependent cities like Denver, young professionals drive themselves to bars, concerts, and sporting events. It's convenient at the start of the night and a problem at the end. Once alcohol or fatigue is a factor, every option is bad:

• Drive home impaired: a serious safety and legal risk.
• Take a rideshare: safe, but you abandon your car and pay surge prices ($100–150 for a 15-mile post-event trip that's normally $30–40).
• Leave the car overnight: next-day retrieval, parking fees, tickets, or towing.

The real gap isn't transportation. It's that no service moves both the person and their vehicle. Users are forced to trade safety for convenience.

The solution

CoPilot dispatches two trained drivers in one vehicle. One driver takes the wheel of the customer's car and drives them home; the second follows in the CoPilot vehicle to pick up their partner afterward. You get home safely, in your own car, and it's there in the morning.

It's fully on-demand through a mobile app, optimized for peak hours and dense nightlife zones to keep wait times low. A planned next step stations drivers at events, so the choice is made for you before the night even ends.

Customer discovery & the pivot

We didn't start with sporting events. The original hypothesis was broad: young professionals who frequently go out would value a service that drove their car home. We tested it before building.

We ran a Qualtrics survey across Denver social circles (ages 21–27 who drink), distributed through social media, group chats, and Facebook groups to map ride habits, pain points, and interest. We paired it with three targeted interviews:

• A bartender: rideshare is the nightlife default, but people want a safe ride in their own car. "Ideally they could hire someone to bring them home in their own car."
• A sports-industry contact: large events spike transportation demand, and "cost is usually number one."
• A skeptical consumer: trust was the dealbreaker, until we framed it as a valet-style service with background-checked, certified, insured drivers. That reframing changed his mind.

What the survey told us

The pivot

After 100+ consumer interviews and surveys, the data pointed somewhere sharper than our hypothesis. Cost and trust were the two levers, and demand concentrated at large events. So we narrowed the target from "young professionals who go out" to sports-event attendees, the crowds at games and big venues, and we re-pointed the whole strategy around a valet-style two-driver model positioned against Uber surge pricing and leading with safety and trust. Next steps were to test pricing with target users and validate demand at live events, then line up stadium pickup-zone partnerships.

Market opportunity

Sized top-down from the global rideshare market and narrowed to a beachhead we could realistically win in year one:

The beachhead: roughly 3% of 22–27-year-olds who regularly attend and drink at sporting events in the Denver metro. Customer discovery showed event-goers had higher planned drinking and a real aversion to retrieving their car the next day, along with more frustration over transportation logistics. That validated the focus on high-density events over general nightlife.

Business model & pricing: my focus area

As Head of Growth & Finance, I owned the revenue model, unit economics, and the full five-year financial plan. We chose a deliberately simple single-stream model for the first five years. The plan is to charge only for rides and perfect the core product before expanding into anything else.

• $40 base fare + $3/mile, averaging ~$85 per ride.
• Priced to undercut surge ($100+ for the same trip) while delivering more value, since your car comes home too and the second trip is eliminated.
• Pricing also guarantees driver pay regardless of trip distance, protecting supply.

Future revenue streams we modeled but intentionally deferred: a priority/discount subscription (à la Uber One), in-app ads, and priority listings.

The financial model

I built the five-year model from the unit economics up. Year one is a six-month ramp (app build, hiring, launch by October), which sets up an average 85.5% YoY growth rate as driver supply, which is the real constraint, scales.

Cost structure

• ~$170K startup costs: app development, equipment, legal/admin, a damage reserve (vehicle-damage insurance), and the marketing launch.
• $16,093/mo fixed costs: software, insurance, TNC permit, marketing, customer support, accounting.
• $52.80 variable cost per ride: paying both drivers plus payment processing; the two-driver model is the defining cost driver.

The model treats qualified driver supply as the binding constraint. About 50 drivers doing roughly 4 trips a day serves around 100 customers a day, which is why year-one revenue is supply-limited well below the obtainable market.

Go-to-market

A venue-by-venue expansion model, where we prove the operation in one place and then scale:

• Phase 1: Ball Arena. Nuggets + Avalanche traffic plus the LoDo nightlife corridor, with a concentrated driver pool, hyper-local marketing, and a referral discount on first rides. Since discovery flagged driver reliability as the #1 concern, operational excellence here is the marketing.
• Phase 2: Red Rocks & Empower Field. Large out-of-market concert and Broncos crowds who drive in and face the exact post-event dilemma; pursue venue partnerships for designated pickup zones.
• Phase 3: metro-wide nightlife. Mission Ballroom, RiNo, and Capitol Hill, driven by word-of-mouth and bar/restaurant partnerships.

Funding plan

CoPilot sought $700K to cover startup costs, tech, and working capital through year one:

• 40% angel investors ($280K). Beyond the capital, angels bring strategic connections to venues, regulators, and driver networks.
• 40% presales & customer financing ($280K): validates demand before the first ride and funds growth with customer dollars instead of dilution.
• 20% founder bootstrapping ($140K).

The team

Four co-founders, each owning a function. I led growth and finance; Brycen Adams (CEO) led concept and problem-solution fit; Trey Teteak (Head of Product) drove product and partnerships; Jordan Schwartz (Head of Operations) led market and competitive analysis.

What I took away

Building the model end-to-end forced real rigor. I had to price against a competitor's surge behavior and size the market from both the top down and the bottom up, then pressure-test the plan against its true constraint, which is driver supply rather than demand. The clearest lesson is that a sharp beachhead and honest unit economics matter more than a big TAM slide.