data_entry_ops · ecommerce · workflow
Advertima accelerates video data labeling 10-15x with super.AI
Advertima's cashierless checkout solution required large-scale video labeling, but their open-source tool CVAT was slow, buggy, and had long turnaround times, generating significant overhead when ingesting proprietary camera data.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Video footage submitted via API
Customer video footage is plugged into super.AI's API for processing.
Tools used
super.AICVAT
Outcome
super.AI processed hundreds of hours of video footage 10-15 times faster than CVAT with greater accuracy.
What failed first
CVAT, the open-source video labeling tool Advertima was using, proved too slow and buggy with limited UX and long processing turnarounds, making it unfit for their proprietary camera footage volume.
Results
Time saved10-15 times
Volumegreater accuracy
Source
https://super.ai/case-studies/digital-signage-company-accelerates-video-labeling-with-super-ai
Grounding & classification
Source type: vendor customer story
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computer visiondata extractionfailure mode describedmetric backednamed customertools describedworkflow describedretailaccuracy improvementcycle time reductionthroughput increasevendor customer storydata entry opsdocument to record