FogHorn Lightning™ is the only “real” edge intelligence solution in the market today, powered by a hyper-efficient Complex Event Processor (CEP). It delivers comprehensive data enrichment and real-time analytics on high volumes, varieties and velocities of streaming sensor data, and is optimized for constrained compute footprints and limited connectivity.
FogHorn’s VEL, an edge-optimized expression language, allows machine learning (ML) models to be edgified (pushing the models to the edge of the network), reducing model size by up to 80%. This approach moves ML as close to the data source as possible, and enables faster and more efficient execution of the model in highly constrained computing environments. PMML is also supported.
Processing the majority of industrial sensor data at the edge radically reduces data transport and storage costs. FogHorn’s subscription, not consumption-based pricing further boosts savings. Additionally, less networking and security resources are needed. This approach is especially powerful in data intensive use cases, and ideal to handle the growing use of video sensors.
FogHorn’s Lightning is cloud agnostic, facilitating hybrid and multi-cloud deployments. This avoids cloud “lock in”, increasing bargaining power and reducing sourcing costs. Many use case specific factors together can make one cloud solution much more cost effective than others, so keeping your options open is critical for current and future needs.
Foghorn Lightning is essentially a non-intrusive operational analytics and intelligence layer that runs on top of existing control systems. It provides an OT-centric toolkit for authoring and expressing events of interest (related to condition monitoring, predictive maintenance, failure detection, etc.). The tools easily enable expressing operator’s tribal knowledge into deriving actionable insights.
Designed to run on existing industrial control systems and highly constrained edge compute devices, FogHorn enables real-time analytics and ML as close to the data source as possible. This minimizes investments in heavy compute or new industrial control systems, and the software can be right-sized based on available compute, from MBs of memory to multi-core servers.
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