Capability brief
Production AI built on signals you actually own
Most AI roadmaps stop at the model card. We start where the information is created: pixels, pulses, and waveforms from hardware we help specify, read out, and calibrate. That is why our applications behave differently under drift, edge constraints, and regulatory scrutiny.
Shipped products, not slide decks
We operate live products such as FVrad and Field Viewers Mammography AI—systems where inference, UX, and operational logging must coexist with real users and real liability.
Those products inform how we design training data contracts, release gates, and monitoring: the same discipline we apply when we embed models inside OEM instruments or security workflows.
MLOps that respect deployment reality
Our MLOps patterns cover reproducible training, staged promotion, drift checks, and rollback paths suited to regulated and mission environments—not only to “accuracy on a holdout set.”
When your model consumes signals from custom ASICs or FPGA front-ends, the interface between firmware bitstreams, host drivers, and feature extraction is part of the model boundary. We engineer that seam explicitly.
- Versioned datasets tied to acquisition conditions and calibration state
- Inference packaging for edge, on-prem, and controlled cloud topologies
- Human-in-the-loop hooks where clinical or operational review is required
Why the vertical stack matters for learning
A classifier is only as honest as its input distribution. When the same team that designed the detector contact stack and readout chain also trains the network, label noise from misunderstood physics drops sharply.
That integration is especially valuable in photon-counting imaging, spectroscopy, and low-SNR detection—domains where naive augmentation or public datasets teach the wrong invariances.