Why It-Jim, and not the alternative.
A vibe-coded demo, an ML hire, a freelancer or an SDK gets a camera or sensor feature working on clean clips. Most stop when real users arrive. Here is where each one stops, and what we do instead.
Every next step starts only if the numbers say go.
“The biggest cost in computer vision isn't the tech. It's the gap between assumptions and reality.”
Ievgen Gorovyi, PhD, Founder and CEO
A weekend demo is not a working feature.
1 of 8Project example: for a fitness app's rep counter we ran 8 open-source pose models on the client's recordings. One held up on fast reps. Even that one was not good enough for the PoC and had to be fine-tuned.
With It-JimWe keep what works in your prototype and rebuild only what fails.
Two months to hire. A layoff when you pause.
~$160ka year, salary alone, for one ML engineer in the US.
With It-JimA team from week one. Pause between rounds without letting anyone go.
One person. No back-up.
1 personcarries the model, the deadlines and the knowledge.
With It-JimA company since 2015, with a Tech Lead and a delivery manager on every project. What we learn is documented, shared across the team and handed over to yours.
A written verdict, the risks we see, the next step.
One focused question, answered with numbers on your recordings. Go or no-go.
A working demo on your data, accuracy before and after, the MVP roadmap.
The first version in your users' hands.
Both platforms, in your repositories, with handover.
Retraining and an accuracy report against the release baseline.
Hover a stage to see where to start.
Will a phone camera or a sensor even catch this?
Months in, the model still does not work.
We are raising. Development has to wait.
The feature has to work in the app, on the phone.
Reviews say it counts wrong.
A new sport, a new test, a version for clinics.
They count as your asset with investors and buyers, and can run on your own infrastructure.
We never name a client without written permission.
API terms exclude training on your data, or the models run fully local.
A shared repository from week one, and Slack with the team between reviews.
Targets are set together once the first results show what your data allows.
A no in two weeks costs far less than a build that was never going to work.
Bets are time-boxed, for example 50 hours on the most promising approach. A PoC contract separates what is guaranteed from what is only explored. Slips are reported before the deadline.
Our 2D and 3D vision leads and the Head of R&D stand behind them.
We run a joint sprint with your engineers when it helps.
Coaches, musicians or clinicians tell us what counts as a mistake.
Model care runs monthly, and you can cancel any month.
Knowledge is shared across the team, so nothing depends on one person.
You own the code, so you are never locked into our choices.
Most failures happen at the joins. We cover all five layers.
90% fewer OCR errors in production.
Neurological tests on iPhone, nothing leaves the device.
Sports tracking: 15 to 120 frames per second.
Hand tracking lost hands gripping sports gear. Fixed with data.
LiDAR cuts room measurement error from 5 to 10% to 1 to 2%.
Describe the feature and where it breaks today. An engineer replies.