Neural networks on the phone itself.

The default architecture for mobile AI is a thin app in front of a server: the device records, uploads, waits, and displays. That costs a network round-trip on every use, fails without a connection, and — the part that matters most in sensitive domains — means the data leaves the device at all.

We inverted it: the entire inference path lives inside the device boundary. Real neural networks are compiled to run on the phone's own Neural Engine — the dedicated ML silicon already in the hardware — so the model runs where the data is, and nothing is transmitted, because nothing has to be.

The frontend is the part most teams reach for a library to do — we wrote it by hand. The raw signal is transformed into a log-mel spectrogram — the time-frequency representation the network consumes — by our own signal-processing code, tuned for the device rather than adapted from a server-side toolkit. That hand-written frontend feeds the network on the Neural Engine, and the whole path — signal in, structured result out — completes in roughly 200 milliseconds, with zero server round-trips.

The figure above traces that path as built: the signal entering the hand-written frontend, the network on the Neural Engine, and the result — all inside the dashed device boundary that nothing crosses.

The same on-device stack also reads physical documents straight into structured records — the capture-to-structured-data path running under the same rule: on the device, offline, nothing leaving.

This is our own product — engineered, shipped, and maintained by us, not a client engagement. It's the proof that we build at the edge for ourselves with the same discipline we sell: when the data can't leave the device, the intelligence has to live on it.