Where We Apply
Pesgit Engineering focuses on sectors where specialist engineering in edge AI and computer vision creates real value, working with organizations that need measured capability delivered on real hardware.
Seven sectors, each with a constraint that only measured performance on real hardware can answer: line speed in inspection, thermal duty cycle in energy, control loop timing in robotics, offline operation in agriculture, unattended reliability in infrastructure, throughput in logistics, and sustained duty cycle in monitoring.
Industrial Inspection
On a production line, the latency deadline is set by line speed, not by hardware capability. A vision system must complete classification before the part leaves the field of view. A system that fails to keep pace misses the inspection without signalling failure, and downstream quality depends on a measurement that was never taken. Spec-sheet compute figures do not answer whether the system will keep up; a measurement at production speed on the target hardware does.
Energy and Solar
Assets in the energy sector operate continuously under ambient temperatures and solar loading that push embedded hardware into throttling regimes a controlled bench measurement never reaches. A system characterized at room temperature may deliver a fraction of its benchmarked throughput under midday solar load on a summer installation. Acceptance testing and sustained operational measurement are not interchangeable; they answer different questions.
Robotics
A robot's perception stack shares a power bus with motors, actuators, and communication hardware. The thermal envelope is fixed by the platform. The latency requirement is set by the control loop, not by a performance preference. A model that runs within budget in isolation may fail under combined system load, and a system that meets its latency target in a static test may miss its control loop deadline on a moving platform under real operating conditions.
Agriculture
Agricultural vision systems operate in heat, dust, and vibration without reliable connectivity, and must be economically viable against the manual labour they replace. A system that depends on a network connection for inference is a controlled-environment prototype, not a field sensor. A system that exceeds the cost per unit of the labour it replaces will not be adopted regardless of its accuracy. Both constraints must be verified by measurement rather than assumed from a specification.
Infrastructure
Remote infrastructure assets cannot be monitored by presence, and a failure that goes undetected may run for days before intervention is possible. An edge vision system at an unmanned site must operate reliably without attention, without connectivity, and without a scheduled maintenance visit. Operational tolerance for undetected degradation is lower at a remote site than at a staffed facility, and the cost of a failure nobody is present to see accumulates before it is caught.
Logistics
A vision system in a logistics facility must match the throughput of the line it monitors and integrate into an existing workflow without creating a new operational dependency. Integration failure is the most common cause of unsuccessful logistics deployments: a system that requires a separate confirmation step, or that introduces latency into a handover process, will be routed around. The operational test is not accuracy in isolation; it is whether the system fits the workflow without adding friction.
Security and Monitoring
Continuous monitoring requires sustained performance, not peak performance. A system that achieves high accuracy in a 30-minute test may degrade significantly over hours of thermal accumulation in an enclosed housing. In many environments, transmitting raw video off-site is a data handling constraint as much as a bandwidth one, and on-device processing is not an optional optimization but a requirement. Both constraints must be verified by measurement under representative operating conditions.