Startup Looks to Move Machine Intelligence to Net Edge
As industrial applications continue to push technology investments ranging from networking to data analytics, a data science startup behind a “mesh intelligence” platform designed to close the “machine-to-human” gap said it has closed an early funding round.
Alluvium, a New York-based startup founded by data science pioneer Drew Conway, said Wednesday (Dec. 7) it has completed a $2.5 million seed funding round led by IA Ventures, Lux Capital, and Bloomberg Beta along with Cloudera co-founder Mike Olson.
The startup’s mesh intelligence approach seeks to leverage machine-learning processes at the network edge where company founders argue human operators most need real-time data. The machine-learning venture is currently running pilot projects designed to apply mesh intelligence to applications such as fleet management as well as oil and gas exploration.
“The commoditized big data stack is fundamentally broken for complex industrial operations,” Conway asserted in announcing the funding round. “Modern industrial assets and hardware are continuing to be instrumented by [equipment suppliers] who have not considered how these heterogeneous streams of machine data should be leveraged in the overall workflow and data strategy of the organization.
“The modern analytics ‘stack’—where data is moved and crunched in backend systems—does not meet the real-time requirements of human operators at the edge,” Conway added.
Hence, Alluvium said its platform is designed to extract data from various nodes in industrial operations, including tablets and sensors. The startup’s approach would enable machine learning processing at the edge of systems and deliver data to operators in real time.
Company backers said the startup seeks to make the jump from merely capturing and storing huge amounts of industrial data to leveraging machine learning at the network edge to figure out what it all means. Or as one investor put it: The startup is looking for ways “complex industrial systems could be transformed by predictive engines.”
The mesh intelligence approach also targets the nearly $2 billion in economic value forecast to be generated by Internet of Things (IoT) devices and asset tracking platforms used for applications such as fleet management.
The startup said it also has gained “early traction” in the oil and gas sector where unplanned downtime at refineries can reach nearly $2 million per day and much higher for liquid natural gas drilling. The expectation is that gathering and analyzing real-time data from industrial equipment would help reduce or eliminate costly downtime.
The emergence of machine learning startups focusing on industrial applications confirms recent assessments on the state of IoT technology rollouts. IoT efforts have been mostly “overhyped and [have] under-delivered,” according to a status report released in November. However, industrial IoT investments remain strong, ushering in what the report called the “Factory 4.0 era.”