Edge-to-Cloud
Manufacturing Intelligence

layline.io connects to industrial equipment through IoT gateways that translate industrial protocols to standard interfaces. Use HTTP/REST endpoints, UDP streams, Kafka topics, or WebSocket connections to receive data from your existing gateway infrastructure.

For custom integrations, build JavaScript adapters that parse proprietary data formats and normalize sensor data into unified event streams. The platform handles connection pooling, retry logic, and data transformation automatically.

Deploy edge clusters close to PLCs and sensors for low-latency processing, then stream processed events to cloud analytics systems. All protocol configuration is version-controlled and deployable across multiple factory sites.

Yes. layline.io runs as a fully autonomous cluster at the factory edge on industrial PCs, edge gateways, or ruggedized servers. Real-time processing (anomaly detection, quality control, equipment monitoring) happens locally with zero cloud dependency.

When network connectivity is available, edge clusters automatically sync processed events to cloud storage (S3, Azure Blob, Google Cloud Storage) and enterprise systems (MES, ERP, data lakes). During outages, events queue locally on disk with configurable retention policies.

When connectivity returns, the cluster automatically reconciles state and backfills missed events. Production lines never stop because of internet failures—critical decisions happen at the edge in milliseconds.

Configure ML inference pipelines using layline.io's workflow designer. Connect sensor streams (vibration, temperature, acoustic, current draw) to your trained models via REST APIs, gRPC endpoints, or embedded Python/JavaScript processors.

For real-time anomaly detection, deploy models as containerized microservices alongside edge clusters. Stream sensor data through sliding windows (5-second, 10-second, 1-minute), calculate FFT spectra and statistical features, then invoke model inference endpoints with low latency.

When models detect anomalies or predict failures, configure automatic actions: create work orders in CMMS systems, send alerts to maintenance teams, adjust machine parameters, or halt production lines. Model updates deploy instantly across all factory sites without downtime.

Yes. layline.io's distributed architecture scales horizontally across cluster nodes. Edge deployments on industrial PCs can handle hundreds of simultaneous sensor streams generating high event volumes per factory.

For larger facilities, deploy regional edge clusters per production area (Assembly, Welding, Painting) with centralized cloud aggregation. Each cluster processes local sensor data with low latency, then streams aggregated metrics to enterprise systems.

Data retention is configurable: keep raw sensor data for 7-30 days at the edge for root cause analysis, while streaming aggregated metrics (OEE, energy consumption, quality metrics) to cloud data lakes for long-term analytics and regulatory compliance.

Configure bi-directional event streams between physical assets and digital twin models using layline.io's workflow designer. Sensor events (temperature, vibration, position, tool wear) flow from equipment to twin models for state synchronization and predictive simulation.

Digital twins run optimization algorithms and \"what-if\" scenarios, then send control commands back to physical equipment through layline.io workflows. For example: adjust CNC spindle speed based on tool wear predictions, modify assembly line timing for throughput optimization, or trigger maintenance windows before predicted failures.

Event sourcing architecture maintains complete state history for time-travel debugging and compliance audits. Replay production events to understand root causes of quality issues or simulate alternative scenarios to optimize future production runs.