Sertn is built for computer vision in environments where failure is expensive.
In high-stakes environments, a model prediction is not just a prediction.
In safety monitoring, one frame can carry a lot of context: a person, a zone, a movement pattern, a missing hard hat, or an unsafe interaction.
Manufacturing vision is not just about finding defects. It is about understanding whether the right part is in the right place, touched at the right m
EU AI Act readiness will not be solved by policy documents alone.
How do you monitor aircraft movement without drowning in false alerts?
2/ At Inference Labs, we think trust has to become part of AI infrastructure itself.
1/ AI infrastructure is moving from abstract cloud story to local reality: power grids, land use, water, and public pushback.
Botnet crackdowns show how much modern abuse now depends on scale, automation, and hidden infrastructure. AI systems will face the same problem.
In inspection workflows, a model output is only useful if the team can trust it later.
Workplace safety is visual, contextual, and often happening in motion.
Airport operations are full of small but important visual signals: aircraft, engines, jet bridges, ground crew, tugs, and movement around restricted a
AI coding agents boosting PRs is exciting, but more code from more agents also means more decisions to review. The question becomes who changed what,
Sertn has crossed 3.1B+ proofs.
Detection tells you what is present. Segmentation tells you exactly what matters.
In traffic and mobility, computer vision can track vehicles, lane behavior, unsafe movement, congestion, and near-miss patterns.