Stellabits is building machine learning that brings specialist-grade interpretation of complex images to the edge — where there's no radiologist, no lab, and often no signal.
Aspirational capabilities in active development. Not a medical device; not for clinical use.
Recognition is largely solved for everyday images. The frontier — and the value — is making models trustworthy in high-stakes, real-world conditions.
Specialized domains don't have billions of examples. A rare finding may have dozens — each labeled by a scarce expert, not a crowdworker.
A model tuned on one scanner, sensor, or season quietly degrades on the next. Brittleness under distribution shift is unsolved at scale.
A raw score isn't enough for high-stakes reads. Without calibrated uncertainty, you can't safely route the hard cases to a human.
The scan happens in a rural clinic, a moving unit, or a remote field — offline, far from the specialist who could interpret it.
Our approach (in development) is designed around one goal: a reliable read you can act on, on-device, with a clear signal of how confident it is.
Complex images — volumetric, hyperspectral, gigapixel — normalized across devices and conditions.
Multi-scale neural features surface structure and signal that's invisible to the untrained eye.
Anomaly detection, semantic segmentation, and classification localize what matters and outline it precisely.
Every output carries an honest uncertainty estimate — so borderline cases are flagged for a human in the loop.
The whole pipeline is engineered to run offline, on a tablet or device, in real time — no cloud required.
Pipeline shown is representative of our development roadmap and uses standard, well-established ML techniques. Not a cleared medical device.
Medical imaging is our beachhead. The same core adapts to any domain where complex images hold decisions that can't wait.
Specialist-grade reads of scans and point-of-care imaging — designed to support clinicians where specialists aren't available.
Turn satellite and aerial imagery into actionable insight at scale — across seasons, sensors, and geographies.
Automated quality control that catches defects before they escalate — precision detection on the production line.
Imagine a mobile clinician in a rural community capturing a point-of-care scan — with no radiologist for a hundred miles and no signal. That's the world we're building for.
Engineered around HIPAA-aligned handling and on-device processing.
Security and auditability designed in from the start, not bolted on.
Calibrated uncertainty means the model knows when to defer.
Runs where the cloud can't reach — offline, in real time.
We're partnering selectively as we build. If your work depends on complex images where the expert isn't in the room, let's talk.