Andreas Savvides explains what NUCAVA (often written as Nucava.ai) is trying to unlock for small and mid-size manufacturers: turning messy factory-floor signals into usable operational intelligence, without needing to ship everything to the cloud first. The core idea is high-quality data collection across OT systems, then edge/on-site processing for low-latency insight, better control, and faster iteration. https://nucava.ai/
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A practical way to read this is “OT/IT convergence without the overhead”: pulling data out of PLC/SCADA islands and point solutions, normalizing it, and making it queryable as time-series so you can compute OEE, downtime reasons, scrap/rework, cycle-time variance, changeover loss, and early-warning indicators. Once the data model is consistent, you can layer anomaly detection, predictive maintenance signals, and process mining on top, and you can do it close to the line where latency and uptime constraints actually matter.
He also frames the problem as a widening gap: AI capabilities are moving fast, while many plants still struggle with basic telemetry, data quality, and siloed historians. The “upgrade” isn’t only AI models; it’s mostly process improvement enabled by better instrumentation, tagging, context (asset, batch, recipe, operator, shift), and governance so that analytics don’t collapse under noisy signals or inconsistent semantics.
This conversation was filmed at Embedded World North America 2025, and it’s a compact snapshot of where industrial AI is headed: edge-first pipelines, secure on-prem deployment, and “insights in the loop” that translate into scheduling tweaks, parameter tuning, and maintenance actions on the factory floor.
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