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The conventional data pipeline wasn't built for the age of real-time AI.
Every second, billions of sensors capture the state of our physical world from factory floors and power grids to human health. The promise of AI is to turn this torrent of raw analog data into intelligent, instantaneous action. But there's a fundamental flaw in how we try to achieve this.
We're stuck with an outdated approach
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This rigid pipeline, a relic of the centralized computing era, creates a massive digital bottleneck that can be inefficient and unscalable. It's holding back the true potential of AI at the edge. To endow our world with real-time intelligence, we don't just need faster digital chips—we need a fundamentally smarter approach.
Massive Energy Waste at Conversion
Before analysis, all raw data passes through power-hungry ADCs — a “digitization tax” that makes always-on AI too power-intensive for always-on battery-powered devices.
Data Overload in Storage and Networks
We handle terabytes of redundant data so AI can find only a few useful kilobytes, overloading storage and networks and driving up cost and latency.
Delayed Insights from Latency
By the time data is digitized, transmitted, and analyzed, critical moments often pass, making true real-time control and fault detection impossible.
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