Scale video. One engine, Every job.
One engine.
As many videos as you need.
Everyframe is video software we built and host in Europe. It takes footage you already own and returns finished video at the volume the work needs. Every example below is a real run on real client footage.
Ship the footage without clearing every face in it.
369 faces found across 186 frames, all pixelated except the one rider you want on screen. He is held by his track identity, so no face recognition and no boxes drawn by hand. The same run also exports a blacked-out version.
369
Faces detected
186 frames
1,410 award videos. Nobody opened an editor.
One design and one list of entries. Every video came back finished in its own region and voice, with zero manual re-edits.
235 entries / 6 voice variants / 13 regions
1,410
Finished videos
One cell, one rendered video
Every lift counted. Nobody wearing a sensor.
The engine reads 17 joints per person from the camera already on the floor. It finds the repeating motion on its own, so there is no rule to write per exercise and no hardware to buy, fit or charge. Give it footage that does not repeat and it refuses to count. It will not guess.
17
Tracked joints
No sensor worn
Six more clips the same run counted
Vehicles read from the air. The video never leaves the drone.
A Jetson board on the aircraft does the seeing at 17 W, fully offline. Home gets about 68 bytes per record and never a frame, so the link stays usable on a bad day and the footage stays where the rules say it has to stay.
68
Bytes per record
17 W on the board
34
Frames carried by the filter
out of 250
The lock, frame by frame
Left is the drone feed with the tracker's box drawn on it. Right is the same target magnified. The coloured box is the raw reading for that frame, and the colour says which confidence gate it cleared. The dashed box and the ring around it are the engine's guess and how far off that guess could be. Watch the ring swell when the detector loses the car, then snap tight the moment a reading comes back.
20 frames clear the high gate. 196 are low confidence and kept anyway. 34 have nothing to read, so the filter carries the box. That is all 250 frames, at a mean confidence of 0.38 .
What happens when the target fades out.
Holding one vehicle is the hard part. A car drops below what the detector can see and the box keeps moving anyway. Confidence gates decide what still counts as the same vehicle, and a filter carries the box forward while there is nothing to read. A solid box is a reading and a dashed box is the engine's guess.
“Perception, not force.” The engine reads a scene and hands it to a person. A human stays in every decision.
Seven passes the engine has run on real footage
Why one engine covers all of them.
Other tools read a JSON template on their own servers every time a video runs. Everyframe compiles a typed description into one portable program. That is compiler work, and it is what every run above rests on.
Models are typed functions. A detector goes from video to detections. If one model's output matches the next one's input they chain. If it does not, the pipeline does not compile.
Typed before it runs
Mistakes surface when the pipeline is compiled. A batch of 1,400 videos does not fail on the 900th video.
One portable artifact
The same compiled program runs in our cloud, inside your own SDK, or on a machine with no network at all.
Three organisations. Over a thousand finished videos.
- GZS Inovacije Video presentations built from what each participant submitted, for the national innovation awards.
- Varnoška Workplace safety drill videos, one variant per site.
- Faculty of Logistics Redaction on training footage before it is shown to students.
Different examples, the same engine behind all of them.
Nothing on this page is a mockup. Every showcase is a run on footage a client already had, and the numbers beside each one come from that run.