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 — each labelled a strong, possible or low match rather than a bare percentage — 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.
