SIG ML builds platform for fast growth

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SIG Machine Learning founder Sam Bost (left) and analytics engineer Jackson Searle
‘It’s hard to be the best-in-class at more than one thing so we focus on the application layer’

The founder of Australian industrial artificial intelligence technology start-up, SIG Machine Learning, remains upbeat about growth prospects in mining despite reported ongoing challenges with successful AI implementations.

“The high failure rate is due to several factors including poor data quality, poor business rationale, lack of domain expertise, insufficient resources and resistance to change,” says Sam Bost, who established SIG ML at the end of 2019.

“Simply, solving the wrong problems without the right people.

“On a more positive note, trends are emerging to tackle these challenges head-on.

“For instance, there is a shift towards defining problem statements more clearly, creating dedicated project teams with the necessary expertise and improving change management.

“Many organisations are opting for a hybrid approach that combines internal and external resources to increase the chances of success.

“By bringing the right people onboard for each project deliverable, they can maximise the probability of success and overcome the obstacles faced by AI projects.”

Bost graduated from the University of Adelaide in 2017 with a degree in petroleum engineering and worked with Shell and Origin Energy. He was only able to commit full-time to SIG ML’s development early in 2021 and hired his first full-time employee just over a year ago.

Despite the constraints of COVID and connecting with new clients in targeted mining and energy markets, Bost wrote in February of SIG ML breaking through A$1 million in revenue in its current financial year and of sales tripling in the past two years.

The firm was recently recognised as one of CORE Innovation Hub’s Hot 30 innovators in Australia.

Its current clients include OZ Minerals and Arrow Energy.

SIG ML’s core technology is the NEXGINEER suite, used in predictive asset maintenance, process automation and intelligent control applications. Bost says the most impactful application to date has been SmartPCP, “an industry-first AI-augmented advanced process control application for pump systems.

“The SmartPCP is currently operational on Kelvin AI’s industrial automation platform,” he says.

SIG ML has partnered with San Francisco-headquartered Kelvin AI, as well as Amazon Web Services and AWS Partners, on recent projects.

“With these partnerships we often deliver projects with five or more dedicated, specialised resources,” Bost says.

“The purpose of these partnerships is to stay focused on our core business and deliver results for customers on accelerated timelines.

“It’s hard to be the best-in-class at more than one thing so we focus on the application layer that codifies discipline knowledge in a software solution and partner with leaders from different technology fields.”

Bost says key mining and metals focus areas included autonomous operations, smart asset management and data-driven exploration.

“Rather than allocating deep domain experts to shallow thinking tasks, organisation resources can be better utilised with technology that leverages AI to see more shallow-thinking automated [tasks], and more deep-thinking enabled [tasks],” he says.

SIG ML is shooting for multi-year application deployments by the end of the year.

Bost says the company is also aiming to “validate our hybrid ML-augmented mineral targeting methodology with an economic intersection”.

“Being able to transition from proof-of-concept (POC) to production-grade software can be challenging, particularly when looking to anticipate future growth or enhancement opportunities,” he says.

“Fortunately, our network of technology partners has helped us along the way and allowed us to stay focused on our core business.

“We need to ensure the scalability of our resources to meet the requirements of emerging projects.

“While POCs bring promising opportunities they require short-term resourcing commitments, and we have to manage our internal resources carefully to support concurrent projects and strike a balance between short-term productivity with long-term sustainability.”

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