Edge Computing
Edge Computing

Decisions at the edge.
Latency. Resilience.

Edge gateways that run analytics locally on the shop floor — predictive maintenance models, quality outliers, control loops. Offline-capable; sync with cloud when connected.

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Edge AI

inference

Offline

capable

Low

latency

Buffered

sync

How It Works

Edge Compute Flow

Sensorraw data
Edge Gatewaybuffer + infer
Decidelocal rule
Actalert + control
Syncto cloud
Retrainmodel update
Edge Gateways
Edge Gateways

Edge Gateways

Compute at the shop floor.

Industrial edge gateways run analytics close to the sensors — sub-millisecond inference, no cloud round-trip. Critical for control loops and real-time alerts.

  • Industrial-grade edge hardware
  • Sub-millisecond ML inference
  • Local data buffering during outages
  • Auto-sync when connectivity returns
  • Hot-swap failover support
  • Remote diagnostics + updates
Edge ML Inference
Edge ML Inference

Edge ML Inference

AI models. Local.

Deploy ML models to edge for predictive maintenance, quality outlier detection, energy optimization. Cloud trains; edge infers; data flows back for retraining.

  • TensorFlow Lite / ONNX models
  • OTA model updates
  • Per-device model versioning
  • Inference monitoring + drift detection
  • Cloud-to-edge model deployment
  • Per-model accuracy tracking
Offline Resilience
Offline Resilience

Offline Resilience

Mill keeps running.

When internet is down, edge keeps the mill running — local data, local rules, local decisions. Buffered data syncs when connectivity returns.

  • Hours-to-days of local buffering
  • Local rule execution offline
  • Cached master data
  • Conflict resolution on resync
  • Per-edge data quotas
  • Bandwidth-aware sync scheduling

Every Feature

Complete capability matrix.

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Preview — available on requestRoadmap — planned within 12 months
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Edge Gateway Hardware

Industrial gateways with ruggedized enclosures, redundant power, multi-network connectivity. Designed for harsh shop floor environments.

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Edge ML Inference

Run ML models at the edge — predictive maintenance, quality anomaly, energy optimization. Sub-millisecond decisions; no cloud round-trip.

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Local Buffering

Hours to days of telemetry buffered locally. No data loss during connectivity outages; sync resumes automatically.

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Model Lifecycle

Train models in cloud, deploy to edge via OTA. Monitor inference accuracy; retrain when drift detected.

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Local Rule Engine

Configurable rules execute locally — 'if temp > X and pressure > Y, alert operator and shut motor'. No cloud dependency for safety-critical decisions.

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Smart Sync

Bandwidth-aware sync scheduling — full sync on Wi-Fi, summary-only on cellular, queue when offline. No bandwidth burns.

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Edge Security

Each edge has unique identity + certificate. Encrypted at-rest storage; revocable on compromise.

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Remote Diagnostics

Cloud dashboard for every edge gateway — health, latency, model accuracy, storage, errors. Triage without truck-rolls.

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Anomaly Detection

Edges detect local anomalies before they propagate. Faster response than cloud-only architectures.

Integrations

Works with everything else.

Every Edge action flows into the other modules — no manual data re-entry, no reconciliation pain.

Edge→IoT

Sensor data → edge

Local ingestion + inference

Edge→Maintenance

Edge anomaly → WO

Predictive trigger

Edge→Production

Quality outlier → halt

Real-time control

Edge→AI

Cloud-trained models → edge

OTA model deployment

Paper mill

Ready to modernize your mill?

See Papyrus BPApp
in your mill.

Book a personalized demo. We'll walk through every module relevant to your operation — from Deckle optimization to GSTR-3B compliance.

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