S m a l l S p a r k . U K

Manufacturing

  • Real-time anomaly detection minimises costly equipment failures.

  • Automated QA ensures consistent, high-quality production.

  • On-site processing cuts cloud dependency and reduces costs

  • Ultra-low latency enables near-instant decision-making

Smarter, More Efficient Production with Intelligent Edge Computing Industry Challenges

Manufacturing is evolving with automation and smart systems, but real-time data processing still presents major hurdles. Key challenges include:

  • Undetected Equipment Failures – Machines can develop faults that go unnoticed until they cause costly breakdowns.
  • Inefficient Quality Assurance (QA) – Defective products can slip through traditional inspection methods, leading to waste and rework.
  • High Cloud Dependency & Costs – Constantly sending data to the cloud for processing increases expenses and introduces latency, reducing efficiency.
  • Fragmented Data Across Systems – Massive datasets from different sources often remain siloed, making it difficult to extract meaningful insights.
  • Delays Due to Cloud Latency – Cloud-based processing introduces delays that can slow down critical decision-making.

How SmallSpark Solves These Challenges

At SmallSpark, we enable manufacturers to process data at the edge—where it’s generated—rather than relying on slow and costly cloud services. Our CORTEX platform and AXON edge computing units provide:

  • Anomaly Detection on Equipment – Intelligent, real-time monitoring detects irregularities in machinery performance, allowing preventative action before breakdowns occur.
  • Automated Quality Assurance – Data-driven models analyse production outputs, identifying inconsistencies and defects in real-time to maintain high-quality standards.
  • On-Site Data Processing & Fusion – By processing and fusing data from multiple sources directly on the factory floor, manufacturers gain deeper insights into operations without costly cloud reliance.
  • Ultra-Low Latency Processing – Edge-based systems reduce data processing delays by over 90%, ensuring near-instant fault detection and response.

  • Real-World Applications

    • Predictive Maintenance – Intelligent monitoring tracks equipment health, predicting failures before they cause disruptions.
    • Data-Driven Quality Inspection – Advanced data fusion techniques compare live sensor inputs against historical patterns to detect production anomalies instantly.

  • Key Benefits

    ✔ Reduced downtime through early fault detection and proactive maintenance.
    ✔ Improved product quality with real-time, data-driven QA.
    ✔ Lower operational costs by reducing cloud service dependency—up to 75% cost savings in operations.
    ✔ Ultra-low latency with edge processing that outperforms cloud solutions by over 90%, enabling instant decision-making.
    ✔ Greater independence with on-site processing and advanced data fusion, enabling manufacturers to infer insights from massive datasets.

Get Started

Take control of your manufacturing processes with SmallSpark’s intelligent edge computing solutions. Request a demo today to see how our technology can streamline operations, reduce costs, and improve efficiency.