How We Build Production AI, Not Lab Demos.
These five principles aren't a mission statement — they're constraints that show up in every model shipped to a production line or a store floor. Each one exists because a more generic approach was tried, on a real deployment, and didn't hold up.
Edge or Cloud GPU Inference
Inference runs on GPU hardware — either on-premise at each location, on GPU-equipped edge devices, for the lowest possible latency; or centrally in the cloud, fed by an on-site VMS (Video Management System) that streams camera feeds up for customers who would rather not invest in edge GPU hardware at every location. Which model fits is a deployment decision made per customer, not a fixed architecture.
“Where inference runs is a deployment decision, not a fixed architecture — latency-critical actions favor the edge, lower upfront hardware investment favors the cloud.”
Purpose-Built Detection Models
Custom-trained object detection models built per use case — attribute extraction, PPE verification, OCR — rather than one generic model stretched across every problem. Each of the 75+ production algorithms in the library is scoped to a specific operational signal.
“A model trained for one job outperforms a general model asked to do many.”
Multi-Object Tracking & Identity Continuity
Object tracking maintains consistent identity across frames — the foundation for anything that depends on duration or journey, from queue dwell time to end-to-end drive-thru vehicle tracking.
“Detecting an object in a frame is different from knowing it is the same object as five seconds ago.”
Zero-Touch Pipeline Design
Where the physical workflow allows it, inspection triggers automatically — a component entering a defined bounding box on a conveyor line starts the pipeline with no manual capture step, as in the SPDR continuous PDI pipeline.
“The most reliable inspection step is the one nobody has to remember to perform.”
Metadata-First Architecture
Only structured JSON metadata — bounding boxes, OCR text, classifications, timestamps — leaves the edge device by default. Raw video stays local unless specifically retrieved for an evidence clip, keeping bandwidth and data exposure minimal.
“Send the enterprise what it needs to act, not everything the camera recorded.”
See these principles applied to a specific deployment in AI in Action, or browse the capabilities they produce in the Algorithm Library.
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