The most revealing object in Linda Gray’s startup story may be a paycheck. It arrived after nearly two years of building Mastertech.ai without paying herself. The amount was smaller than any paycheck she had received in her career, a career that already included Microsoft and Niantic. She joked with her husband that they should frame it. The joke had an engineer’s precision: a tiny number could still represent a large change of state. For the first time, the company she had chosen over a secure salary could pay her something.
Gray had spent years making software for millions of people. Mastertech.ai asked her to make something useful at the precise moment a technician confronted a stubborn cooling fan, an obscure service bulletin, or a wiring diagram with all the charm of a subway map drawn during a power cut. The scale looked smaller from the outside. The information problem was not.
“You have to put the product first. You have to put the customers first.”Linda Gray, Gain Traction podcast
A fan page with a long memory
Gray was born in China and moved with her family to New Jersey when she was 10. Her father became a software engineer working on trading systems for financial firms near Wall Street. What she noticed first was not the glamour of code. It was the stress. If a critical system stopped, money disappeared by the second. Her teenage conclusion was sensible: choose anything else.
Then the early web made software look less like an alarm and more like a room. At 16, she built a New York Knicks fan site. She watched the games, went to games, and kept building. The site grew from a fan page into a forum with hundreds of thousands of posts and lasted seven years. Gray later called it her first startup, with the affectionate quotation marks people reserve for projects that taught them more than their formal title suggests.
The Knicks site introduced a pattern that would return in an auto shop decades later: a group of people produces valuable knowledge simply by doing what it does. The hard part is giving that knowledge somewhere to live and a way to be found. A forum remembers an argument about a point guard. A repair database remembers that a fan control module solved a symptom on a similar vehicle. Different uniforms, same architecture.
Learning scale, one product at a time
Gray studied at Cornell University from 2002 to 2006. She joined Microsoft as an intern around 2004, then returned full time after graduation. Over the next 15 years she moved through software engineering, principal engineering, and management. Her work crossed Outlook Web, search, document collaboration, Xbox Live Family, and the app ecosystem around Microsoft Teams.
Teams arrived at a peculiar moment in her career. During the pandemic, remote work and online education turned collaboration software into ordinary infrastructure. Gray managed engineering teams that worked on the platform’s app ecosystem, helping outside services connect inside Teams. The job joined technical plumbing to visible human consequence: a feature could determine whether a class or a meeting held together.
She also designed and shipped Color Dash, an Objective-C color-mixing puzzle game for iOS with more than 100 levels. Her public profile records more than 5,000 downloads and a five-star App Store rating. It is a smaller artifact than Teams, which is exactly why it belongs in the story. Gray could work within a huge organization and still enjoy taking a product from blank file to people’s phones.
Leaving the map
In 2021, Gray joined Niantic. She became director of engineering for Pokémon GO and described the work as having a positive impact on millions of players. Two years later she left. Her public farewell carried no faux certainty. She was excited, she wrote, and “maybe more than a little bit scared.” She had wanted to start a business for as long as she could remember. Now she intended to do it.
Her co-founder, David Forman, supplied the knowledge she did not pretend to have. He brought more than 20 years as a master technician and shop owner. Gray brought software systems, product development, and engineering leadership. Forman knew how a repair bay actually loses time: the research, the scattered factory information, the differences between models, the diagnostic paths that live in the memory of an experienced technician. Their partnership gave Mastertech.ai its useful constraint. It had to make sense to the person holding the wrench.
The paperwork was trying to talk
Independent shops can encounter an enormous range of vehicles. Gray has described more than 60,000 unique models on the road, each with its own specifications, components, known issues, and procedures. Meanwhile, modern vehicles keep accumulating software and electronics. A technician’s work now includes substantial information research alongside the physical repair.
Mastertech.ai treated a shop’s records as more than administrative residue. Historical repair orders contain symptoms, tests, diagnoses, and confirmed fixes. If a new car arrives with a cooling fan that keeps running after shutdown, the system can search similar work, show which causes appeared most often, and let the technician inspect the underlying repair order. OEM procedures, vehicle histories, technical service bulletins, wiring diagrams, and shop data add more context.
Gray’s metaphor was “bionic.” The software would extend the technician’s memory with collective experience. That word matters because repair work is consequential and stubbornly physical. A language model cannot hear a bearing, probe a circuit, or accept responsibility for a repair. It can help a trained person narrow a search, surface a buried bulletin, or recover the solution documented by a colleague years ago.
The organizational value may be even larger. When a senior technician retires, years of tacit knowledge can leave with them. Well-written repair orders offer a partial defense. Gray compared the result to multiplying a shop’s best master technician by 100 and keeping that knowledge available as a central memory. The image is cheeky. The management idea is serious: expertise should compound rather than evaporate.
“We’re just really here to want to make a difference in this industry, and we really feel like we can do it.”Linda Gray, Gain Traction podcast
The founder rewrites her own manual
Gray’s first two startup years did not resemble a tidy founder montage. She later wrote that she initially treated the company like a grind and assumed pain was evidence that she was working hard enough. She followed other people’s rules and playbooks. Eventually, gratitude replaced some of the grimness. Few people, she reasoned, get to build a company and attempt useful change in an industry.
Her preferred measure became value: what the team built, how it served customers, and whether the business could become financially independent. She borrowed a lesson from Man’s Search for Meaning: aim directly at success and you may miss it. Do work you believe in and let success arrive as a side effect. It is a philosophy with a convenient dislike of vanity metrics, but Gray did not use it to excuse the commercial work. Shops still had to choose the product. Payroll still had to run.
She has also spoken publicly about technology’s capacity for harm and the pressure on companies to optimize growth, revenue, and engagement even when those goals pull against an original mission. Her position is neither nostalgia nor blind enthusiasm. Innovation can solve difficult problems. The people building it still owe users and society a careful account of its effects. In a field full of declarations that AI changes everything, Gray keeps returning to a plainer question: does it help the person doing the job?
A company changes hands
In 2026, AutoVitals announced that it had acquired Mastertech.ai. The combination is practical: AutoVitals already provides digital inspections, workflow tools, customer management, websites, and marketing to independent repair shops. Mastertech.ai adds diagnostic and service intelligence built from OEM material, vehicle history, shop systems, and confirmed repair data.
Gray is joining AutoVitals to help guide product strategy and the integration of the platforms. Existing Mastertech.ai customers are due to continue using the product while a deeper native integration is developed. The planned direction includes overdue maintenance recommendations, diagnostic support, estimate assistance, and easier access to the technical information that technicians and service advisors need inside their workflow.
For Gray, the acquisition closes one founder chapter without ending the product problem that drew her in. She wrote that Mastertech.ai had partnered with hundreds of repair shops and that she was proud of the impact the team made. The next assignment is distribution: bring the service intelligence to more shops without sanding away the attention to how those shops work.
There is a pleasing line through the whole career. A teenage forum preserved what Knicks fans wanted to say. Outlook and Teams moved messages and work between people. Pokémon GO coordinated millions of players around a shared digital layer on the physical world. Mastertech.ai made the memory of a repair shop easier to retrieve. Gray keeps building systems that help groups remember, connect, and act.
Away from the screen, her stated obsession is cooking, especially Asian food with spice and flavor: Chinese, Japanese, Thai, Indian, Malaysian. The preference feels appropriate. Gray’s professional recipe has never been one clean ingredient. It is large-company discipline, side-project curiosity, a technician partner’s experience, old repair orders, and the nerve to accept a smaller paycheck because it came from something she chose to build. Now the recipe belongs in a larger kitchen. She is staying to make sure it still tastes like the shop.