Predictive maintenance platforms, smart sensor networks, IIoT system integrators. You're running distributed systems on factory floors where a single production line failure costs €100K+ per hour. That demands aerospace-grade operational thinking.
Your platform runs on factory floors where downtime stops production lines. The same edge-to-cloud architecture patterns that challenge satellite operations — latency, autonomy, reliability — are your daily reality.
Hundreds of edge nodes across dozens of factory sites. Each running inference models, aggregating sensor data, making real-time decisions. When an edge node fails, production decisions go blind. You find out when the quality team calls.
Sensor → edge → cloud → analytics → action. A 7-step pipeline where any break means delayed or wrong decisions. Nobody has mapped the failure modes end-to-end. Data quality issues propagate silently until they surface as bad predictions.
You deployed ML models for predictive maintenance. Who monitors model drift? Who validates predictions against outcomes? Who decides when to retrain? MLOps in industrial contexts requires operational discipline, not just DevOps tooling.
Your platform is powerful. Your customer's maintenance team has 3 people and a WhatsApp group. The gap between platform capability and operational readiness on the factory floor is where value dies.
Each customer site is different: different equipment, different sensor configurations, different integration points, different SLAs. Managing operational consistency across 40 heterogeneous deployments without a framework is unsustainable.
IT security practices don't map to OT environments. Patch cycles, access management, and network segmentation all work differently when you're touching production-critical systems. One wrong update stops the line.
"Edge-to-cloud distributed systems running on factory floors follow the same patterns as satellite ground systems. Telemetry ingestion, health monitoring, anomaly detection, autonomous response — it's the same architecture."
Concrete operational engineering engagements adapted from aerospace mission operations to industrial IoT and smart manufacturing operations.
Design the unified operational view across all customer deployments. Edge node health, data pipeline status, model performance, SLA compliance — aggregated into a single operations centre display that lets a small team manage dozens of industrial sites.
Operational overhead per customer site reduced 65%
Apply FMECA across your entire data pipeline: sensors, edge compute, network, cloud ingestion, processing, storage, and analytics. Map every failure mode, score criticality by industrial impact, design detection and recovery for each path.
Undetected data quality issues reduced from 12/month to 1/month
Design the operational framework for managing ML models in production industrial environments. Model monitoring, drift detection, performance validation, retraining triggers, human-in-the-loop approval gates, and rollback procedures for safety-critical predictions.
False positive rate reduced 40%, model retraining cycle time halved
Design the behaviour of edge systems when connectivity fails. What decisions can the edge make autonomously? How does the system degrade gracefully? What's the recovery procedure when connectivity returns? Based on satellite autonomous operations patterns for when ground contact is lost.
Production continuity maintained through 99.5% of connectivity outages
Build the operational playbook for deploying and managing your platform across heterogeneous industrial sites. Site assessment, deployment procedures, integration testing, handover to customer ops, ongoing support model, and escalation paths.
Deployment time reduced from 6 weeks to 10 days with higher first-time quality
Every engagement follows the same structured methodology, adapted from aerospace mission assurance processes.
Structured assessment using the MCRF framework across all six reliability pillars. Map your current operational maturity, identify critical gaps, and score against industry benchmarks. 2-3 weeks.
Design the target operational architecture: monitoring topology, incident response flows, automation boundaries, team structure, and tool requirements. Prioritised implementation roadmap. 2-4 weeks.
Hands-on implementation of operational processes, runbooks, dashboards, and team workflows. Training, game days, and operational reviews until the team runs independently. 1-3 months.
We work with companies building or operating IIoT infrastructure where platform reliability directly impacts industrial production, safety, or customer trust.
A structured conversation about where your operational maturity stands — and what it would take to reach mission-critical reliability.
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