What we do
Consumer AI
We build apps that listen, look and answer: a tutor that corrects your English as you speak, a phone that cleans your camera roll, a camera that counts your meal. We build and run each one ourselves. Three are live on the App Store today.
See the productsEnterprise AI adoption
We find the work AI can take off your team, build it into the systems you already run, and stay until people actually use it.
Talk to usConsumer AI
Real-time speech
An AI English tutor you speak to out loud. It corrects you mid-sentence, so Mongolian speakers learn to be understood.
On-device vision
Finds duplicates, blurry shots and screenshots with on-device AI. Nothing ever leaves your phone.
Domain-tuned vision
Photograph the plate. Coach reads it, counts it, and knows 501 Mongolian dishes by the name you use.
Why Us
Every product we ship solves a problem someone actually has - and ships fast enough to matter.
AI is not a feature we bolted on. Nuva listens and answers out loud. Album sorts a camera roll on the phone itself. Coach reads a plate of food from one photo. Take the AI out and there is no product left.
People open these apps on an ordinary Tuesday: Nuva on the commute, Album when the storage warning appears, Coach at lunch.
Small team, no approvals to chase. A bug someone reports on Monday goes out in that week's build, not next quarter's roadmap.
Every engagement crosses the same ground: the product decisions, the model integration, the architecture, and the work that keeps it running after launch.
We build an AI product end to end - the workflow, the model calls, the interface, and the release.
We connect models to the systems you already run, with your data and your permissions.
We design the screens people use every day, then test them with real users before release.
We keep your data where you want it. Album is the extreme case - it classifies photos without the phone sending one.
We tune for the phones people actually carry - cold start, battery, and patchy networks.
We test on real devices before release, and watch crash and performance data after it.
Short loops. You see working software early, not a deck.
We look at what your team does by hand, and pick what a model can take off them
One workflow, running on your data, in weeks
Real users, real load, and the fixes that come out of it
Training, rollout support, and changes as the work changes
