A Borrowed Volvo Saved His Father’s Life. Not Everyone Gets Access to That Volvo.

Rashid Galadanci grew up between northern Nigeria and southern New Hampshire — two places, in his own description, where economic trickle-down effects weren’t really reaching people — which shaped a lasting interest in how entrepreneurship and business can genuinely lift people out of poverty. His path to Driver Technologies runs through management consulting, a large microfinance bank (FINCA International), a family office, and a venture fund investing in social impact — including a position in CloudFactory, an AI data-labeling company serving machine learning firms, autonomous vehicle companies among them.

That vantage point put him close enough to the autonomous vehicle hype cycle around 2016 to grow genuinely skeptical of it — and gave him a very personal reason to care about the underlying problem. As a child, Rashid’s father was in a serious car accident in Nigeria after borrowing a wealthier friend’s imported Volvo — reportedly one of the only Volvos in the country at the time. The car that hit them T-boned the vehicle; the occupants of the other car died, while Rashid’s father survived, something he always attributed directly to being in that specific, safety-equipped car rather than a typical vehicle on Nigerian roads at the time. That combination — awareness of car safety technology’s life-saving power, and awareness of how unequally that technology is actually distributed — became the founding thesis of Driver.

In Episode 71 of InsurTechTalk, Rashid and I covered how Driver turns any smartphone into an AI-powered safety dashcam, why he bet against the mainstream 2016 consensus that full autonomy was right around the corner, and what happens inside an insurance claim once dashcam and telematics data enter the picture.

About Rashid Galadanci

Rashid Galadanci is CEO and founder of Driver Technologies, an AI-driven mobility company that has built a smartphone dashcam application designed to democratize advanced car safety and connected-vehicle capabilities. Before founding Driver in 2018, he worked in management consulting, at the microfinance institution FINCA International, and in venture capital focused on social impact and entrepreneurship, including an investment in AI data-labeling company CloudFactory. Driver has raised just over $10 million to date and employs 24 full-time and six part-time team members distributed across the US and internationally.

Tesla-Level Safety, Democratized to Any Phone

Driver’s core product is an AI-driven dashcam app — the top search result for “dashcam” in the app store, per Rashid — that uses a phone’s external camera to monitor the road and its internal camera to monitor the driver for drowsiness and distraction, the two leading causes of car accidents and driving deaths. Using computer vision comparable to what’s built into a $100,000 vehicle like a Tesla or Mercedes S-Class, the app detects cars, pedestrians, cyclists, motorcycles, buses, and trucks, alerting drivers when it senses an unsafe interaction combined with signs of distraction or drowsiness — functioning not just as a recording device after the fact, but as an active attempt to prevent the accident from happening at all.

The mission framing behind this is explicitly about access: car accidents are a top-ten global cause of death, causing over a million deaths and 50 million injuries annually, and Rashid’s pointed observation is that advanced safety technology tends to arrive only at the very top of the vehicle market — while the average US car on the road is roughly 12.5 years old (and considerably older in many other markets), meaning that gap between available technology and actual deployed technology is arguably getting worse, not better, over time.

The Zebra (and the Deer That’s Actually Coming)

One lighthearted but revealing detail: Driver’s object-detection model includes zebras as a kind of internal team joke, added during early model development and triggered for real when a team member on a Florida wildlife drive-through had an actual zebra cross the road. The more practically important version of that same capability is deer detection, which Rashid said the team was actively working to incorporate. His broader point: while traditional automakers can take years to add a single new detectable object class to a forward-collision system due to internal engineering and safety-validation cycles, Driver’s software-first architecture lets the team add new detectable object classes far faster.

Why Cheap Dashcams Don’t Actually Compete With Driver

Asked about the obvious comparison to inexpensive dashcams already on the market, Rashid laid out the category clearly: basic dashcams without night vision or safety features typically run $50-100; better cameras run $200-250; GoPro-level devices run around $400; and a further tier of dashcams with genuine safety capabilities (companies like Nexar, Mobileye, and Nauto among them) serve customers, often large fleets, who can justify a serious fixed investment.

Driver’s own thesis targets a different failure mode entirely: many users come to Driver after burning through four or five cheap hardware dashcams that failed, offered poor low-light performance, or simply died with no real customer support behind them — commodity hardware sold through Amazon with a factory somewhere in China standing behind it. Driver’s ambition, in Rashid’s words, is a “Zappos-type experience” for in-car technology: real human customer support, plus a cloud-based experience (the “Driver Cloud”) that automatically uploads trips and lets users selectively share specific clips — with an insurer, a boss, friends, or on Reddit — along with rich telemetry (speed, g-force, weather, location) that a standalone dashcam simply doesn’t provide.

Where Insurance Enters: A “Mic Drop Moment” for Claims

Rashid and I first met at an InsurTech Hartford symposium, and the insurance angle traces back to Driver’s Series A, which included an investment from Liberty Mutual Strategic Ventures — a relationship that connected Driver directly to Liberty Mutual’s claims team. Rashid quoted a specific line from someone on that team describing what video evidence, paired with Driver’s motion, trajectory, speed, location, and accelerometer data, does to a claims dispute: a “mic drop moment.”

His concrete illustrative example: a classic rear-end collision, where a driver in front does something unpredictable (an aggressive lane cut, hard braking) and gets rear-ended — a scenario traditional insurance defaults to treating as automatically the rear driver’s fault, regardless of what actually happened, especially if the driver in front isn’t inclined to be truthful about the sequence of events. With video and full telemetry, that ambiguity disappears — Rashid noted the effect extends even to how differently police officers on the scene treat the interaction once they’ve seen the footage. On the claims side specifically, the practical impact is speed: a dispute that might otherwise take six months and potentially end up in court can often resolve in a matter of days once clear video evidence is available — saving the insurer money and saving the driver significant time and stress, regardless of whether that driver is a regular commuter, a rideshare driver, or part of a commercial fleet.

Growth Without Marketing: 50,000 Drivers, 170 Countries

At the time of recording, Driver had grown to roughly 50,000 users across the US and had been downloaded in 170 countries globally — achieved, Rashid was clear, with minimal deliberate marketing spend. The company had been running small partnerships, fleet pilots, organic downloads, and early marketing experiments to learn what resonated, with a full Driver Premium subscription launching within the app that month, and a more deliberate commercial push planned for the end of the quarter.

Driver Premium includes cloud storage and trip-sharing, a nationwide roadside assistance program (built on a partnership with Honk, an LA-based roadside assistance company, delivering an Uber-like experience with automatic driver reassignment if the original driver falls through), gas discounts through the Booster program on GasBuddy, expanded safety alerts, and a carbon-neutrality offset program in development that would let users plant trees to offset their own driving-related emissions, or sponsor offsets on someone else’s behalf.

Growth Loop: A Gamified Driver Score

Rashid pointed to word of mouth, powered by a built-in gamified driver score, as a meaningful organic growth driver — family members competing to have the best driving score in the household, which naturally pulls in additional users. His broader framing of the opportunity: enormous public spending has gone into anti-distracted-driving and anti-drunk-driving awareness campaigns, but very little has gone into giving individual drivers real-time, constructive feedback on their own driving — delivered in a way that doesn’t feel like surveillance, using the computer vision capability already sitting in most people’s pockets, to coach drivers on concrete, quantifiable improvements (his example: following one additional car length behind the vehicle ahead reduces rear-end collision risk by roughly 87%).

Building a Distributed Team Around a Hard, Meaningful Problem

Asked how he — someone without a traditional technology background, though he built the app’s first version himself before stepping back from coding — assembled a team capable of solving a genuinely difficult multi-disciplinary AI problem (computer vision for moving and static objects, facial/drowsiness detection, and vehicle telematics all at once), Rashid credited the mission itself as the primary recruiting advantage: the combination of genuine social impact and a real, hard technical challenge attracts experienced engineers who might otherwise be difficult to recruit into a startup. His recruiting approach leans heavily on personal networks and persistence — his own CTO is a high school friend he pursued for three years before they eventually joined forces. Beyond raw experience, he specifically looks for people with a positive, can-do attitude and strong interpersonal skills, given that most of the team works remotely and disagreements over deeply technical decisions are inevitable.

Driver’s team, 24 full-time and six part-time at the time of recording, is spread from New Hampshire to New Jersey along the US East Coast, across the Midwest and West Coast, and internationally across China, Hong Kong, Italy, Portugal, Malaysia, Egypt, and Pakistan.

Betting Against the Autonomous Vehicle Hype Cycle

Rashid’s account of Driver’s actual origin story is a deliberate contrarian bet. Around 2016, the dominant narrative — Elon Musk’s included — was that fully autonomous, low-cost, shared self-driving vehicles were imminent, available to everyone within a year or two, and poised to save millions of lives worldwide. The deeper Rashid studied the space (through his venture and CloudFactory work), the more that timeline looked, in his words, like “marketing.” Betting against full autonomy’s near-term arrival was a genuinely unpopular position in 2016 — pitch after pitch, he said, was met with some version of “but we’re all going to have autonomous vehicles soon” — and it wasn’t until roughly 2020 that broader industry sentiment shifted to acknowledge autonomy was a much harder problem than widely assumed. Rashid’s founding question, once he committed to that contrarian read: what can actually be done to save lives today, rather than waiting on a promise that kept slipping further into the future?

Recommendations: Breakfast Salad, and Y Combinator’s Free Curriculum

Asked for a closing recommendation, Rashid offered two. A lighter one: “breakfast salad” — starting the day with a bowl of arugula, berries, and nuts instead of toast, on the theory that a genuinely healthy start to the day makes the rest of it easier to manage, however it goes. His more substantive recommendation was a shoutout to Michael Seibel, CEO of Y Combinator and Driver’s first investor, who Rashid credited with taking a real chance on what Seibel himself described at the time as a “wacky idea” and a “terrible pitch.” Rashid’s broader point about YC: despite seven years in venture capital, sitting on multiple startup boards, and 20 years of business experience, he found he genuinely didn’t understand what running a company actually required until he did it himself — and YC’s teachings, freely available on YouTube even without the program’s network access, were genuinely revelatory to him even with that level of prior experience.

Key Takeaways

• Driver’s dashcam app brings Tesla- and Mercedes-level computer vision safety technology (object detection, drowsiness and distraction monitoring) to any smartphone, explicitly targeting the gap between advanced safety tech and the average 12.5-year-old car actually on US roads
• Driver differentiates from cheap commodity dashcams not on hardware alone but on a cloud-based experience and genuine customer support — many users arrive after multiple failed cheap devices with no service behind them
• A Series A investment from Liberty Mutual Strategic Ventures connected Driver directly to Liberty Mutual’s claims team, who described combined video and telemetry data as a “mic drop moment” that can resolve disputes (like default-fault rear-end collisions) in days instead of months
• The company grew to roughly 50,000 US users and downloads across 170 countries with minimal marketing spend, driven substantially by a gamified driver score that creates organic, family-level competitive sharing
• Driver Premium bundles cloud storage, trip sharing, nationwide roadside assistance (via a Honk partnership), gas discounts, and a planned carbon-offset program into a subscription layered on top of the free core safety features
• Rashid’s founding thesis was a deliberate, unpopular-at-the-time bet against the 2016 consensus that full vehicle autonomy was imminent — choosing instead to democratize existing, provable safety technology to reduce deaths and injuries today rather than wait on a longer-term autonomy promise
• Recruiting a highly technical team was built substantially around mission-driven appeal combined with genuine technical challenge, leaning heavily on personal networks and sustained persistence rather than conventional hiring channels