Why Cloud AI Cannot Stop a Forklift in Time
The physics the marketing slides skip
A safety alert is only worth anything if it arrives before the thing it warns about happens. That makes latency the whole game, and latency is governed by physics, not by how good a model is. A forklift moving at 10 miles per hour covers about 14.6 feet every second. A worker stepping out of a blind corner has perhaps a second of margin. Every fraction of that second your system spends moving data around is margin you do not get back.
Where cloud latency actually comes from
When a camera ships a frame to a cloud service for analysis, the delay is the sum of several stages that each cost real time: the camera encodes the frame to H.264 or H.265, the encoded stream traverses the local network and the internet uplink, the cloud ingests and decodes it, the model runs inference, and a result travels all the way back to whatever device is supposed to act. On commercial networks that round trip commonly lands somewhere between 1.8 and 2.4 seconds, and it gets worse, not better, when the network is congested or the uplink is contended.
What that delay costs in feet
At 10 miles per hour, two seconds of latency is roughly 30 feet of forklift travel after the hazard was already visible in frame. The system was right. It was just too late. No amount of model accuracy recovers a decision that arrives after the collision.
Why edge-native inference changes the outcome
DHI processes video on an edge node beside the camera. The frame never leaves the building, so the encode-transport-ingest-return chain collapses to a local classification step. End-to-end detection stays inside 150 milliseconds, which is fast enough to drive a real intervention: an audible warning, a visual signal at the conflict point, or a structured alert into the VMS while there is still distance to spare.
The bandwidth dividend
Local inference also means you are not streaming continuous high-bitrate video to a data center. You send small structured events instead. That is what makes the approach viable on sites with thin uplinks, and it is the same property that lets the architecture run on remote rail corridors and large perimeters where cloud streaming is simply not an option.
The honest tradeoff
Edge processing is not free; it means deploying and maintaining compute at the camera rather than renting it by the API call. The position DHI takes is that for any safety decision tied to momentum, a forklift, a train, a person at a platform edge, that is the only tradeoff that makes sense. You cannot negotiate with physics, and a cloud round trip loses to it every time.