What we know how to do

The hard part is never reading a single channel. It is sustaining an acquisition over time: sensors answering at different rates, a link dropping at the first speed bump, a session ending when the ignition is cut, and files that must stay readable when the program that wrote them no longer exists.

We work with OBD-II over serial and network links, simulator shared memory, and networked microcontrollers for timing. Data comes out in open formats, one channel per file, written to survive an abrupt cut.

A business application is judged on what happens on day three hundred, not day one: what the schema becomes when a field is added, what happens when two people edit the same record, what remains readable once the author has left.

We write integrity rules into the database rather than only into the code, we version migrations from day one, and we build map layers on open components so as not to depend on a vendor's price list.

A model trained on a public dataset fails precisely on the cases that matter: the unlit traffic light, the backlit sign, the part seen from an angle no photo in the set contains. The useful work is upstream, in building the images, and downstream, in the compute budget granted to each frame.

We build and annotate the datasets, we measure where the model fails before merging, and we decouple inference from display to keep things smooth on a machine without a dedicated GPU.