Prototyping is often treated as a demonstration of what is possible, rather than an evaluation of what is practical. When a vision AI concept is prototyped using cloud compute, pristine optical conditions, and static targets, it proves nothing about its operational viability.
We define a field-credible prototype as one that is exposed to the constraints and environmental conditions of the intended deployment. It must run on representative edge hardware, process data from actual sensors, and experience the thermal, vibrational, and optical stress of the target environment.
By building prototypes that are engineered for the field, organizations generate hard evidence of capability, de-risking the technology before committing to scaled development.