Tractable says it is the UK's first computer vision unicorn, after raising a $60 million in fresh funding at a valuation above $1 billion.
The round was led by existing backers Insight Partners and Georgian.
Company president Adrien Cohen said the firm had grown revenue to eight figures, thanks to the startup landing major insurers as clients in the last year.
The London startup, founded in 2014, has primarily applied computer vision capabilities to assess car damage after an accident. It partners with insurers to help make an initial assessment and estimate repair costs, something which can reduce the time a car spends in the body shop. The firm has trained its algorithms on scores of photos of damaged cars, and claims the system is as accurate as a human.
Tractable was cofounded by computer scientist and former hedge fund quant Alex Dalyac, machine learning specialist Razvan Ranca, and ex-Lazada exec Adrien Cohen.
"Reaching this milestone is not important, per se, but it's what it says about the impact and scale of our technology, the validation of reaching this scale," Cohen said of the firm's unicorn status. The startup counts around 20 insurance clients across the US, Europe, and Asia, including Berkshire Hathaway affiliate Geico.
Though initially specializing in auto repair assessments and estimates, the firm is now expanding into analyzing property damage and even car purchasing.
"We're going to go deeper, we think our AI can deal with cases where you want to inspect a vehicle's condition, not just in an accident, so when you purchase, sell, or lease" said Cohen. "All these events, where you can accelerate the process by understanding the vehicle condition from photos."
In theory, the platform could partner with a used-car platform like Cazoo to assess the condition of a car placed for sale. Cohen said auto rental firms and auto manufacturers are also potential clients.
Asked about revenue growth, Cohen said the firm was privately held and would not reveal specific numbers. "It's an 8-figure revenue [number], with 600% growth in the past 24 months," he said, adding that the firm had only raised $55 million in outside capital prior to the new round.
Computer vision startups and the route to commercialization
Tractable is one of a wave of startups benefiting from the maturation of computer vision. According to this year's edition of the annual AI Index, collated with Stanford University, computer vision is becoming increasingly "industrialized."
Alessio Petrozziello, machine learning research engineer at London data extraction startup Evolution AI, says that more broadly computer vision has some hurdles to clear before it goes fully mainstream.
"There's certainly a push to commercialize these models, but it's been clear they are not at the level where you can [fully] rely on them," he said. "For example for a self-driving car, it can't make any mistakes, certainly no more than a human." Apart from accuracy, he added, there's the issue of responsibility. "You use a model, and the model makes a mistake, who's responsible? There isn't a clear-cut answer."
Eleonora Ferrero, director of operations at Evolution AI, added that success for startups like Tractable was as much about execution as the fundamental computer vision tech.
"Their go-to-market was partnerships with key insurance companies that provided data, it's an advantage," she said, adding that Tractable had been smart to identify something that insurers sought — increased operational efficiency.
Karen Burns, founder of computer vision platform Fyma, said adoption depended on clients being ready for the tech. Fyma's platform, trained on anonymized data, analyzes what's going on in a physical space — whether that's a firm tracking the movements of its autonomous robots for safety; or a retailer measuring footfall.
"Before you can adopt AI, you have to go through a big transformation," she said.
Tractable's Cohen agreed, saying that the firm had relied on the quality of its AI development but also selling the usefulness of AI to enterprise clients. "A big playbook we've cracked is how to deploy and capture the value of artificial intelligence in an enterprise context," he said. "This is very challenging, and something we've had to figure out."
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