Almost every guide assumes you already know what's wrong. Search for a rattle, a leak, or a warning light and you get forty forum threads, each describing a different vehicle, a different year, and a different root cause. The hard part was never turning the wrench — it was identifying the thing you're looking at and deciding whether it's the actual fault or just the nearest symptom.
That's a perception problem before it's a knowledge problem. And perception is exactly what modern computer vision is good at.
The obvious way to build fixin would have been to send every photo to a server, run a large model, and return an answer. It's cheaper to build and easier to update. It's also the wrong trade.
Photos of a repair are photos of your property — your driveway, your engine bay, your basement, sometimes your license plate or your front door in the background. Shipping all of that to someone else's computer to identify a corroded battery terminal is a bad bargain for the person taking the picture.
So the diagnosis models run on the device. That constraint shaped everything downstream: model architecture, size budget, how aggressively the engine can be retrained, and how each release gets validated. It is harder. It is the reason the app can work in a parking garage with no signal, and the reason there is no photo of your house sitting in a training bucket somewhere.
Most apps change their models quietly. A result you got last month is not the result you get today, and nothing tells you why. fixin names each generation of its on-device engine instead — Sapphire 4.3 today, with Sapphire 4.0 and Ruby 3.9 still selectable.
The naming isn't decoration. It means:
fixin gives you a ranked set of likely causes with confidence attached, the steps to confirm or rule each one out, and the parts you'd need if it turns out to be right. It is a very well-read second opinion that has seen a lot of photos.
It is not a substitute for a licensed professional on anything involving structural, electrical, gas, or brake safety. The app says so at the point where it matters, not buried in a policy. When a job should go to a pro, hirin is there to hand it off rather than talk you into doing it yourself.
AM Solutions is a small Seattle company, not a venture-funded growth machine. That limits how fast things ship. It also means there's no pressure to monetise attention, sell data, or bolt on features that make a quarterly number look better. The business model is simple and stated plainly on the store page: you buy a license, you get the app.
The near-term roadmap is unglamorous and specific: broaden repair category coverage, get fixin Web out of beta, and expand the hirin network beyond the Seattle metro. Longer term, the interesting question is whether a genuinely private, on-device repair assistant can stay competitive with cloud models as those keep growing. Betting that it can is the whole premise of the company.
Photo-first repair diagnosis for cars, boats, and homes — running on your device, not someone's server.
