Plants, factories, and manufacturers in general are embracing IoT, which in turn is driving the use of artificial intelligence at the edge of corporate networks as a way to streamline industrial processes, improve efficiency and detect maintenance issues before they become problems – perhaps even big problems that could force plant shutdowns.
The AI-powered industrial IoT space already has a couple of its own unicorn startups, such as Uptake with $117 million in funding and a $2.3 billion valuation and C3 IoT with $243 million in VC funding and $1.4-plus billion valuation (the C3 IoT valuation was not publicly updated with its latest $100M round of funding, so its valuation is outdated and conservative).
At the same time, incumbents such as AWS, Dell, and Cisco are pouring billions into IoT and edge computing. HPE recently invested $4 billion on its “intelligent edge,” while Microsoft pumped $6.5 billion into its IoT efforts.
The competition in this sector is brutal, but the opportunity is big enough that the 10 startups highlighted here still have room to maneuver and time to scale up. Keep an eye on them because one or more could well be the next unicorn in this hot market.
What they do: Provide an IoT analytics platform that turns complex streams of data into simple, real-time insights
Year founded: 2015
Funding: $4 million in seed funding from Work-Bench, IA Ventures, Bloomberg Beta and Lux Capital
Headquarters: New York, N.Y.
CEO: Drew Conway, who also co-founded DataKind, a global non-profit, and is the co-author of Machine Learning for Hackers.
Problem they solve: Industrial operations collect more raw data than even expert operators can manually observe and analyze on their own. It can be difficult to determine what data matters and what is just noise. Important but small blips in the data are easy to miss, which can lead to underperformance, downtime and even safety incidents. Missing important signals within the noise ends up being costly to an industrial operation, both in terms of monetary and productivity losses.
How they solve it: Leveraging machine learning and AI, Alluvium helps industrial companies achieve operational stability and improve their production. Alluvium’s flagship product, Primer, uses machine learning to help companies distill complex, massive streams of raw sensor and production data into usable insights.
Industrial teams can rapidly navigate data to identify where and when deviations occur, determine the source of the problem and decide what action to take. Primer creates a Stability Score analysis based on collected data drawn from any timeframe – from last night to, say, the past year. Primer helps operators identify anomalies from sensors, single machines or an entire facility to make the changes necessary to keep operations running smoothly.
Competitors include: OSIsoft, C3 IoT, Uptake, Foghorn, Presenso, Falkonry, and Manna
Customers include: At the time of publication, Alluvium did not have any customers willing to go on the record.
Why they’re a hot startup to watch: Alluvium has locked down $4 million in seed funding, and while their team is small and a bit green (not unusual for an early stage startup), Alluvium has entered a high-growth but confusing space. In such a noisy market, Alluvium’s focus is on simplicity. Alluvium boils down the data generated from complex production systems into a set of metrics that non-experts can understand. The resulting “Stability Score” provides at-a-glance data points operators can use to easily track key variables that could prevent them from meeting business goals.
What they do: Helps heavy industrial companies with complex physical assets improve operations through machine learning and other advanced analytical applications
Year founded: 2015
Funding: $35 million. Their most recent round, a $28 million Series A, closed in the first half of 2018. Investors included Sundt AS, Stokke Industri, Horizon, Canica, Strømstangen, Arctic Fund Management, Stanford-StartX Fund, and Northgate Partners.
Headquarters: Houston, Texas
CEO: Tor Jakob Ramsøy, who was previously a Senior Partner at McKinsey & Company, where he led the technology service lines for the Global Energy Practice and EMEA Big Data/Advanced Analytics. Ramsøy was also country manager for McKinsey Norway and led the Business Technology Office in Scandinavia.
Problem they solve: Industrial companies face unique challenges when attempting to integrate machine learning and other advanced analytical applications into their daily operations. These challenges include managing complex, highly engineered physical assets; dealing with layers upon layers of equipment and instrumentation that has accrued over many decades; and coping with varying levels of control systems, ERP systems and data stores that are often scattered across multiple operating companies, subsidiaries, acquired entities and even third-party vendors.
With that much complexity, ingesting live data and then streaming it to a highly available, cloud- or edge-based machine-learning system is no small task. Even more difficult is pushing out models based on that data in a timely fashion, so the data can inform business decisions.
Once you accomplish that, you must still figure out how to scale machine-learning applications from a handful of models to dozens or hundreds of models across the numerous assets and use cases in a typical industrial operation.
How they solve it: Arundo Analytics automates the end-to-end challenges that emerge when large industrial companies from oil and gas, power and shipping attempt to use edge analytics to drive daily business decisions.
Arundo applies machine learning and advanced analytics to edge data inputs, integrating those inputs into daily business operations and scaling these applications throughout the enterprise. Arundo helps companies deploy machine-learning models to the cloud in order to create enterprise-grade software applications, which the platform then manages.
Arundo also offers configurable, out-of-the-box applications for common industrial challenges, including equipment-condition monitoring, system-anomaly detection and a virtual multiphase flow meter, which the startup developed and jointly markets with the global industrial technology company ABB.
Competitors include: GE Predix, Siemens Mindsphere, ABB Ability, C3IoT and Uptake
Customers include: Equinor, AkerBP, Carnival Maritime, DNV GL and INEOS
Why they’re a hot startup to watch: Arundo Analytics has the second-largest VC haul – $35 million – of all the startups in this roundup. Their leadership team gained experience at McKinsey & Company, Aker Solutions, Siemens and other high-tech and industrial-focused companies. They have a respectable list of early adopting, on-the-record customers, and Arundo’s out-of-the-box industrial applications help manufacturers quickly overcome common headaches such as anomaly detection.
What they do: Provide an AI-powered predictive-analytics platform for industrial applications.
Year founded: 2016
Funding: The first of two seed-funding rounds led by Real Ventures with participation by Barney Pell closed in May 2017, and financial terms were not disclosed. The second closed in July 2018 and will be announced this month.
Headquarters: Toronto, Ontario, Canada
CEO: Humera Malik. She previously served as Executive Director for Quexor Group and Director of Product Management for Redknee.
Problem they solve: Typical industrial environments are data rich but information poor. A plant’s connected assets and dynamic processes can generate hundreds and thousands of data points every minute, but less than 10 percent of this data is used to derive insights or aid decision making.
Instead, decisions are made based on the personal experience of operators and/or by using outdated tools that are unable to handle voluminous, frequently changing data that comes from a variety of sources.
How they solve it: Canvass Analytics’ real-time AI data models identify trends to help operators understand the variables impacting their industrial processes. The Canvass AI Platform responds to changes in the data in real time, providing operations teams with the most up-to-date information so they can continuously adjust their operations to improve quality, reduce energy consumption and optimize asset health.
The Canvass AI Platform simplifies the challenge of rapidly processing large, complex volumes of data by using AI to automate the entire data-science process. The platform distils the millions of data points generated by industrial machines, sensors and operations systems, and identifies patterns and correlations hidden deep within the data to create new insights. These self-learning models adapt to new conditions in real-time ensuring that decisions by operations teams are made with the most accurate data possible.
Competitors include: GE Predix, IBM Watson, Uptake Noodle.ai
Customers include: At the time of publication, Canvass Analytics did not have any customers willing to go on the record.
Why they’re a hot startup to watch: As an early stage startup, Canvass Analytics has locked down two rounds of seed funding to pursue early adopters. The leadership team gained relevant experience at Quexor Group, Redknee, Bell Canada, NorthWest Energy and CHR Solutions. The company has built its platform to continuously ingest massive volumes of data in real-time, which its AI platform uses to improve and then automate operational processes.
What they do: Provide machine-learning software for industrial operations
Year founded: 2012
Funding: $10.9 million. Falkonry closed a $4.6 million Series A in June 2018. Investors included Polaris Partners, Zetta Venture Partners, Presidio Ventures (Sumitomo) and Fortive.
Headquarters: Sunnyvale, Calif.
CEO: Nikunj Mehta. Prior to Falkonry, Mehta was the VP of Customer Success at C3 IoT. Before that, he was at Oracle, where he led the team that created the IndexedDB standard for databases that is embedded inside all modern browsers.
Problem they solve: To compete globally, industrial companies must improve the productivity of their operations and/or embrace new business models, and many industrial companies are turning to data analytics to drive that change.
The data generated in manufacturing or process operations, especially time-series data, is very rich in information that can provide actionable insights on the health of the production systems and the products created.
Machine learning is ideally suited for analyzing such massive amounts of data. However, hiring data-science consultants has proven inefficient, as they lack the subject matter expertise of the operations teams, and, as a result, such projects can take more than a year to see results.
How they solve it: Falkonry applies feature learning and machine learning to multivariate time-series data that is generated by the equipment and production systems in most discrete manufacturing and industrial process operations.
Given the high volume and number of signals, most industrial data goes unutilized in operations today. The Falkonry operational machine-learning system is able to discover hidden patterns in the data that cannot be observed by humans or traditional analytics. These patterns, in turn, provide insights to the operating state, and they identify conditions that precede undesired events to issue early warnings. Depending on the process being monitored, such early warnings may occur hours, days or even weeks in advance.
Falkonry says that its system acts like a “data scientist-in-a-box,” meaning no data scientists are required, and it can be quickly deployed by manufacturing engineers or process engineers. Customers start gaining actionable insights within three weeks of deploying the system, which could potentially save companies millions of dollars annually.
Competitors include: Cylance, Alluvium, Presenso, Seeq, Sight Machin and SparkCognition
Customers include: Toyota Industrial Equipment Manufacturing, Kawasaki Heavy Industries, Ternium, Ciner Resources and Energias de Portugal (EDP)
Why they’re a hot startup to watch: Falkonry has locked down nearly $11 million in funding. Founder and CEO Nikunj Mehta previously served as the VP of Customer Success at C3 IoT, a unicorn IoT startups. Add a customer list that includes the industrial divisions of Toyota and Kawasaki, and Falkonry was a no-brainer for this roundup.
What they do: Provide an edge-computing platforming that connects to any AI system
Year founded: 2014
Funding: The startup is backed by an undisclosed amount of funding from private equity (Telos Ventures) and governmental (National Science Foundation, Department of Homeland Security) sources.
Headquarters: Sunnyvale, Calif.
CEO: David Jung. Previously, he was an engineering leader at both Brocade and Cisco.
Problem they solve: As the IoT trend gains steam, the amount of data generated is becoming so large that AI processing at the edge is transitioning from a nice-to-have to a must-have IoT feature.
Processing data at the edge is difficult, however, because computing resources are constrained. This is why so many AI IoT vendors attempt to push data to the cloud first, which isn’t practical in many industrial applications.
How they solve it: Interactor’s IoT edge software enables companies to apply the latest AI technologies at the edge without any lengthy deployment effort. Interactor acts as small-footprint IoT gateway that sits between edge devices and the cloud. Interactor abstracts all of the components and microservices needed for technologies to interact and/or integrate with one another into a small (about 50MB) executable.
Using Interactor, developers and operators can easily integrate the AI of their choice and apply the intelligence closer to the devices for faster response times.
Interactor IoT edge software can be run on any IoT gateway or server, and it includes pre-packaged device configurations, security and authentication, messaging, device visibility, logging and error handling.
Competitors include: AWS Greengrass, Microsoft Azure Edge, EdgeX Foundry and PTC Kepware
Customers include: Cisco, Panasonic, Malaysian Government and MacroBlock
Why they’re a hot startup to watch: Interactor hasn’t released details of its funding, but its backers include a VC firm and the Department of Homeland Security.