Swim ESP Edge Learning

Machine Learning and the Industrial IOT

Enterprises and public sector organizations are drowning in a flood of sensor data from devices (e.g. mobile sensors, factory systems, supply chain optimization, city data sources, and utility/grid energy delivery) that contain hidden insights with the potential to optimize production, transform efficiency, streamline flows of goods and vehicles, and improve safety. But finding them remains a huge challenge.  

In an attempt to address it, technology providers have developed complex, big-data based, cloud hosted solution stacks and new application development platforms.  But they have failed to recognize that organizations at the network edge do not have the skill sets to develop new apps or manage cloud hosted infrastructure.  Swim solves the problem simply and powerfully: Instead of new big-data apps, Swim discovers insights automatically, at the edge, without requiring new applications and without new IT or OT skill sets.  Swim avoids the need for new application stacks by using powerful, efficient, real-time, distributed machine learning technology at the network edge to dig through dirty data to discover the insights that matter.

Swim ESP™ finds hidden insights by applying self-training edge learning to high volume streams of real-time sensor data.  Swim edge learning takes advantage of the price/performance benefits of Moore’s Law to deliver an affordable, easy to use, secure and fault tolerant edge fabric that quickly delivers value in both brown- and green-field settings, using commodity edge devices.

 Swim ESP edge learning helps organizations quickly find hidden gems in their data:

  • Swim implements distributed local learning: A digital twin of each real-world object learns from contextually relevant data streams, simplifying the learning problem.  

  • Swim edge learning is self-training.  Its algorithms continually check their hypotheses against real world data, training and adjusting for over fitting as needed.  This avoids the need to label training data and avoids any need for machine learning expertise in the OT environment.
  • Swim runs on commodity edge hardware whose price/performance curve is relentlessly improved by Moore’s Law.

  • Swim can learn as much on a device that costs a few hundred dollars at the edge, as a solution costing thousands of dollars per month, in the cloud.

  • Swim uses learning to self-configure and manage, reducing IT/OT cost, training and complexity.

  • Learned insights are available in real-time and in context, helping users or apps to quickly make smart, local decisions.

  • By learning at the edge of the cloud,  close to the systems that generate data, Swim delivers insights at a fraction of the cost.


Swim ESP is a streaming data analytics engine that reasons on-the-fly, taking advantage of a distributed learning architecture to gain efficiency & new insights.