SWIM.AI, the edge intelligence software company, recently exited stealth and announced new additions to its leadership team.  To find out more about SWIM.AI, VMblog reached out to a long time friend, Simon Crosby, who just joined the company as its new CTO. 

VMblog:  For those not yet familiar with SWIM.AI yet, can you give us a brief overview of the company and problem you are solving?

Simon Crosby:  Absolutely. It comes as no shock that smart devices and sensors are being rapidly adopted by businesses, governments, cities and beyond. This has led to an explosion of edge data, with these new edge devices and sensors requiring fast, local analytics and prediction to aid local decision. At SWIM.AI, we aim to deliver powerful business and operational insights from that data for customers. We are enabling businesses to reduce, analyze, learn and predict from ‘gray' edge data on the fly, on existing edge devices. 

VMblog:  SWIM.AI has been in stealth for the past few years, what are you announcing?

Crosby:  SWIM.AI was founded by Chris Sachs and Rusty Cumpston back in 2015, and today we are pleased to not only exit stealth, but also announce the GA of EDX, our flagship software delivering edge intelligence and real-time business and operational insights. We also announced the additions of Simon Aspinall as chief sales and marketing officer, and Ramana Jonnala as chief product officer, along with myself joining as chief technology officer. 

VMblog:  Can you tell us a bit more about the need in the market?

Crosby:  Absolutely. Current business intelligence and analytics solutions assume a central solution that follows client/server approaches that reduce data locally, pipe it to the center, clean it, model it and then apply the central model. But in today's landscape, with so much raw edge data and so many new devices, we need a solution that can make quick analysis and provide actual useful insight. This data can transform production, efficiency, strategy and much else. Its key to an organization's success.

VMblog:  What are the main benefits users can expect from SWIM EDX?

Crosby:  By applying analytics, self-training digital twins and edge computing, SWIM EDX allows organizations to analyze and take immediate action on fast edge data. As I see it, there are three main benefits:  

• Improved Efficiency - Data Intelligence: SWIM EDX reduces costs for transmission, storage and central compute of edge data, through data reduction and efficient use of idle (edge) compute.
• Real-Time Insights - Edge Intelligence: SWIM EDX provides business and operational real-time insights and predictions from gray edge data, enabling asset tracking, preventative maintenance and predictive cost avoidance, as well as real-time process improvements.
• Revenue from Gray Data - Data as a Service: SWIM EDX can turn gray edge data into events, relationships and predictions for valuable third-party information services.

VMblog:  As you mentioned, the company also named some great additions to its executive team.  Can you tell us a bit about each?

Crosby:  SWIM.AI has an outstanding team and for me, joining represents an opportunity to work with world beating system architects, engineers and business leaders. Lead by Rusty Cumpston and Chris Sachs, our team together brings decades of experience in solving deeply complex compute, data and network challenges. Together with the additions of Simon Aspinall and Ramana Jonnala, SWIM will truly shape the future of analytics.

VMblog:  What use cases are ideal for SWIM EDX?

Crosby:  SWIM EDX is incredibly versatile. To mention a few: 

• Manufacturing: SWIM EDX analyzes manufacturing data streams tracking performance, predicting failure, timing maintenance and helping OT optimization.
• Networking: SWIM EDX runs locally on routers, set-top boxes and wi-fi controllers enabling channel selection, network optimization and improved performance.
• Enterprise IT: SWIM EDX runs on existing assets - helping reduce and analyze edge data and IT real-time performance.
• Logistics/Supply Chain: SWIM EDX provides data ingestion, event analysis and asset tracking, providing data deduplication, cost reduction and analysis.
• Traffic Management: SWIM EDX reports real-time traffic conditions and predictions to an API from existing traffic data sources while running on existing traffic infrastructure. Smart Cities & IoT: SWIM EDX enables smart meters, smart grids, smart cities info services, smart lighting, smart parking and much more.
  

VMblog:  Anything else you'd like to share with our readers?

Crosby:  The opportunities for the edge are enormous.  We've discovered two simple things: 

• A powerful and computationally efficient extension to the distributed actor model (eg: Erlang, Orleans, Akka) that enables Actors to learn on their data, and
• An actor-based view of the real world, as recorded in time-series data, with self-training DNN models that can learn and back-propagate on-the-fly.
  

Using this approach, it is easy to analyze, learn and predict the future on commodity edge devices.  We learn an actor-specific model, rather than a system-wide model. This has many advantages: The models are robust, contextually rich, and permit local responses in real-time.

This makes me tremendously excited for the future of edge based learning and analytics.  The cloud is crucial for problems of scale.  The edge is crucial because massive diversity requires local, focused models that can be learned from data.

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About Simon Crosby

Simon Crosby is CTO of SWIM.AI. Crosby joined the company from Bromium, where he was CTO and co-founder. Prior to Bromium, he was CTO of the Data Center & Cloud Division at Citrix. Crosby joined Citrix through the acquisition of XenSource in 2007, which he also co-founded and led as CTO. Previously, Crosby was a principal engineer at Intel, where he led strategic research in distributed autonomic computing, platform security and trust. He was a member of faculty at the University of Cambridge Computer Laboratory and Fellow of Fitzwilliam College.