What you can build with programmable interfaces

Your documents do not look like Docling's training data: a lab report or an internal invoice has little in common with the public PDFs a converter learned on. This interface gives you a place to see where that breaks.

You draw bounding boxes around document elements, layer reading-order paths across them, and correct merged regions and table structure, with the DocLang XML updating in a sidebar as you work. Predictions from the Docling ML backend load as a starting point, and the result exports as a .dclx archive carrying both the structure and the page images.

Most perception teams annotate the same scene four times: once for cuboids, again for camera views, again for tracking, and once more for review. This interface keeps all of it in one workspace.

Annotators work the 3D point cloud alongside a top-down view and a camera panel that follows whatever cuboid is selected. Keyframe interpolation carries objects across frames, so adjusting one position updates the frames around it instead of redrawing each one — which is where identity swaps usually creep in. Model predictions load as editable suggestions to accept, adjust, or reject. Validation runs inside the task, so blocking errors surface before completion rather than in a separate audit pass weeks later.

Handles COPC files in the tens of millions of points, with cuboids, points, lines, lanes, and segmentation masks side by side.

A wearable session is five signals that have to be read against each other: heart rate climbing after the accelerometer, HRV dropping, respiration following, SpO2 holding until it dips. This interface puts all five on one time base with a context track underneath, so precomputed activity, sleep, and rhythm segments stay visible while you work, and outdoor sessions carry a GPS route beside the signals.

Labeling splits across three tabs, and the vocabularies ship as defaults you replace: Running through Sedentary for activity, Awake through Deep Sleep for sleep, Sinus Rhythm through AFib for rhythm, each label with a color and a hotkey.

The task fields are parameters too, so the interface reads whatever your sensor, segment, and route columns are called instead of forcing your data into its names.