Frans Conje builds Dataprophet to become the foundation for autonomous manufacturing

A revolution is underway in the factory using its data-rich environment to leverage artificial intelligence and machine learning to improve productivity and reduce product defects. And it’s a big and growing market. Spending on AI in manufacturing is $2.3 billion in 2022 and is projected to hit $16.3 billion by 2027, according to Markets and Markets.

Dataprophet is one such company that is helping to drive innovation in this market. The Cape Town, South Africa-based company is a developer of machine learning and artificial intelligence technologies serving the manufacturing industry. The company specializes in optimizing the complex manufacturing processes of key industry verticals through machine learning and its AI-based solutions that leverage existing data streams from production line equipment plant to identify process efficiency.

“Our vision is to be the leading provider of impactful AI for the machines that make the world. We are committed to helping manufacturers achieve their Industry 4.0 goals and build a strong foundation for autonomous manufacturing,” says Frans Conje, co-founder and CEO of Dataprophet.

The idea for Dataprophet grew out of Conje’s scientific interest in advanced statistics at the University of Cape Town, his fascination with the work of Geoffrey Hinton, known as the godfather of artificial intelligence, and his business experience at Bain and Company in private equity.

“I joined Bain and Company and really appreciated their practicality in understanding business issues. And so, at that point in my career, I had that foundation in statistics for business, but I felt like I wanted to go a little deeper into the technical space, thinking that you can always get back to business, and I started a path to do my masters in statistics around 2012. At the time, Geoffrey Hinton and the lab at the University of Toronto started talking about the results they had with ImageNet and so, I was headed in that direction, I saw this emerging field with AI and I wanted explore it,” says Conje

He intended to continue the nascent field of AI by building a very good team in the field or joining a good team. It turned out that he built his own team. He got in touch with a few friends who were also beginning to see the potential of neural networks as a technological change and the results that Hinton was getting in his lab at the University of Toronto. And so the founding hypothesis of Dataprophet was centered on the idea of ​​transposing the results of university laboratories into the real world. The company was first incorporated in 2014 as a consultancy helping companies understand machine learning and the potential of AI.

“In 2017, we started working in the manufacturing space. And it’s a fascinating environment. We’ve worked a bit in a few other spaces, but what I really enjoy about manufacturing is the huge amount of data that underpins the deep physical process. No one disputes that this information comes from the physical world,” says Conje. The small team at Dataprophet then transformed what they had learned through their consultancy projects into a software product that manufacturers would use themselves.

“In fact, we like to call it a product because we’re going from the machine to the feedback. This is all a solution, contrary to how I characterize platforms is that you as a customer have to bring users to the platform, and they get value from it. And we’re talking end-to-end prescriptive AI, meaning we ingest data from your machines, end-to-end, and then it starts flowing to your operators without them need to be data scientists, without you need to have specialist knowledge in this area,” says Conje

Today, Dataprophet AI software is used in manufacturing plants around the world. The 50-person team now works with almost all types of major manufacturing regions spanning Japan, China, Europe, North America, South America and in its home country of India. South Africa. Their AI-as-a-service software currently ingests over 100 million unique data points daily across all of their sites. “I think the important aspect is the magnitude of the impacts we’ve had in different environments. So across these different spaces, our customers have an average of 40% impact on production KPIs, whether it’s reducing defects or improving production,” says Conje.

Despite the company’s growth and the success of its clients, it has only attracted a total of $6 million in venture capital funding to date, given its location far from major capital centers. risk. It is a funding round that was raised in 2018 and was led by Life Capital with additional investors Yellowoods Capital, the Industrial Development Corporation of South Africa (IDC) and Norican Group, a leading engineering company and foundry equipment.

Conje grew up in Cape Town, the son of a well-known and successful designer and bespoke furniture maker. But from an early age, Conje was more interested in numbers than business. He went to the University of Cape Town where he obtained a degree in Actuarial Science and where he later obtained his Masters in Statistics.

“A big part of my motivation for starting a business is wanting to see technology come to fruition. And I guess that comes from a lucky upbringing and watching my dad follow his passion and craft. doing furniture making very well because that’s what he really enjoys. He’s very well recognized in the context of South Africa for the work that he does. I think that’s just a bit of an underlying assumption as to how I see the world because I’ve seen it happen in front of me,” Conje says.

He first learned to apply his love of numbers to the business world when he was hired by Bain and Company in their Johannesburg office. He would leave Bain after a year, then reunite the team to co-found Dataprophet in 2014.

As for the future? “The journey we’ve embarked on is to fully realize the technology supporting much more autonomous factories. One of our ambitions is to take it to the point where the intelligence provides instructions to the machine itself, making all the necessary adjustments to ensure the plant is running as efficiently as possible,” Conje concludes. .

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