Australian startup Daisee is stepping closer to its goal of “democratising artificial intelligence” after a recently closing an $8.8 million Series A funding round, despite only launching in August last year.
Daisee, an acronym of Deep Artificial Intelligence for Enterprise Ecosystems, is the brainchild of former Google regional managing director and ex-Friendster chief executive Richard Kimber.
The startup is looking to take proven AI methodology from leading universities and apply it to commercial opportunities, hoping to ditch the “big sticker” price of AI solutions from leading tech companies and make the emerging tech more accessible for medium to large businesses.
It’s Kimber’s first “clean sheet” startup, although he’s been involved with a number of established tech companies and served on the board of startups such as Unlockd in recent years, and this experience has given him some inkling of what to expect. But he told StartupSmart he was still unprepared for the intensity of being a founder.
“I’ve worked at an early stage business and I’ve been around a lot of entrepreneurs, so I figured it was about time I had a go,” Kimber laughs.
“Having been a CEO before I have an idea of what it’s like, but my son did work experience recently and told me CEO should stand for ‘chief everything officer’, and I’m starting to think that’s true.”
“It’s pretty full-on. The biggest challenge is operating at a range of strategic levels, which is a challenge for me and anyone else on a startup journey. I’m used to working at a high level alongside a team of literally hundreds, and now I work with 22 other people so you’ve got to do a lot with a little.”
The $8.8 million raise was led by investment firm Alium Capital and contributed to by Thorney Opportunities and other investors. Raising nearly $10 million for a seven-month-old startup is a significant feat, and Kimber says the raise amount reflects investors confidence in the expertise of the team and the size of the startup’s opportunity.
None of the investment comes from ‘traditional’ sources such as angel investors or venture capital firms, with Kimber saying those traditional sources weren’t as interested in the startup as other types of investors who startups may not usually pursue.
“Lot of family offices and boutique funds in Australia seem to have a good risk appetite and tech focus so we had some success with them, and they were more interested in our story than VCs,” he says.
The funding from the round will be going almost entirely to getting new staff on board at Daisee, with the startup looking for a number of data scientists and engineers, while a small amount will be going into hardware and software development.
Kimber notes the current tech scene in Australia can be talent-scarce but believes the allure of working at a “US-style startup” in Australia will draw some of the AI specialists “buried” in other companies towards Daisee.
Australia still lagging behind in AI adoption
Kimber believes the world is at the early stage of AI adoption, with early forays into chatbots indicating the first stages of acceptance and integration for both consumers and businesses. But “true AI”, as he puts it, is yet to be deployed, with real AI solutions being used by just 9% of current Australian businesses, he says.
Australia needs to pick up the pace on AI adoption, says Kimber, with the country currently lagging behind.
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“Australia is probably still lagging behind a bit, so we’re going to be in a period of catching up. But once we do that, the pioneers will extend their lead, so it’s key for businesses to keep the window open here for AI,” he says.
The founder also thinks there’s a good deal of misinformation in the AI space, and while consumers are becoming more used to the technology through interactions with programs like Siri and Alexa, the bigger uses come from deep integration with business platforms, such as Google’s use for their search algorithms.
“In terms of business opportunities, companies we talk to are yet to grapple with what AI can start to offer. I think those opportunities lie in prediction and forecasting and using AI to identify new opportunities and new ways of going to market,” he says.
For startups looking to get started with AI, however, Kimber recommends a smaller-picture approach.
“Start somewhere where you have the highest leverage — the biggest impact for the lowest effort rather than trying to tackle a giant project. Pick off something to optimise with AI that can have a big impact on how things work,” he says.