EDGE-TO-CLOUD VISION ARCHITECTURE.
The infrastructure that runs Proglint's AI models in production — from an existing camera on a wall to a dashboard on a director's screen. Engineered for flexible edge or cloud GPU inference, structured metadata, and enterprise-scale multi-tenancy.
Deployment Model
Where the GPU Inference Runs Is a Choice, Not a Fixed Architecture
Not every customer wants to invest in GPU hardware at every location. Proglint supports both models, selected per deployment.
GPU-equipped edge hardware installed at the location itself. Detection and the resulting decision happen with minimal delay, with no dependence on facility bandwidth — the right fit when a real-time action (a PLC reject signal, a safety alert) can't tolerate a network hiccup.
For customers who would rather not invest in edge GPU hardware at every site, Proglint deploys a lighter-weight VMS (Video Management System) on-site that streams camera feeds to a cloud-hosted GPU inference engine — lower upfront hardware investment, faster rollout across many locations.
Both models feed the same downstream pipeline — structured metadata into the backend, aggregation and fleet visibility in the cloud, everything surfacing in the Enterprise Command Center. See Edge AI for the full breakdown of when each model fits.
Edge-To-Cloud Data Pipeline
Only structured metadata leaves the device by default — raw video stays local to wherever inference runs unless specifically retrieved as evidence.
GPU-accelerated video analytics pipeline framework, run wherever the GPU inference lives — edge or cloud.
On-premise edge GPU for lowest latency, or cloud-hosted GPU fed by an on-site VMS — chosen per deployment.
Deployed as the runtime inference format — see AI Research for the model engineering itself.
Containerized services for consistent deployment across edge and cloud.
Multi-tenant cloud orchestration across every enterprise account and location.
Decoupled backend services communicating over REST APIs.
Structured integration endpoints for POS, KDS, and PLC systems.
Isolated data and configuration per enterprise account at global scale.
This page is about infrastructure — not the models
Everything above is the pipeline a detection travels through: camera, GPU inference, backend, cloud. It says nothing about how a specific model was trained, what it actually detects, or why it's architected the way it is. Curious about the AI models themselves rather than the infrastructure they run on?
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