Which edge hardware does Edge‑Sync support?

Deployments span NVIDIA Jetson‑class devices, industrial IPCs, and k3s fleets. Bajpai Labs runs hardware–software co‑design sprints when thermals or power budgets are tight.

How much cloud cost can we realistically remove?

Programs target material reductions by shifting steady‑state inference to the edge while keeping cloud for training and orchestration. Exact savings depend on payload shape and WAN behavior.

What happens when the device loses network connectivity?

Offline‑tolerant execution modes and federated update channels keep devices productive, with drift telemetry once links recover.

Pilot flagship · Pilot available

Edge-Sync (Edge Inference)

A framework that pushes heavy model execution to cameras, gateways, and plant-floor devices, cutting cloud spend dramatically while keeping quality where the data originates.

THE C‑SUITE HEADACHE

"Our cloud inference bill balloons every time we scale pilots, but edge feels impossible to operationalize."

Up to ~70% cloud inference cost reductionHardware–software co-designSevere cloud cost takeout

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Capabilities

Run heavier models on constrained edge hardware without ballooning cloud bills.

Model partitioning across edge + cloud lanes

Quantization and adaptive precision schedules

Federated update channels for device fleets

Offline-tolerant execution modes

Telemetry for drift and hardware health

Boutique hardware–software co-design sprints

Use cases

Retail vision and loss-prevention

Factory sensing and predictive maintenance

Smart buildings and campus security

Logistics yard monitoring

Integrations

Targets factory, retail, and IoT footprints where bandwidth and latency matter.

NVIDIA Jetson & industrial IPCsAWS IoT GreengrassAzure IoT EdgeMQTT / OPC-UA collectorsKubernetes K3s fleets

Shift thinking to the edge.

Edge-Sync is a distributed inference framework built for cheap devices and serious savings.

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