Home Technology Tractable doesn't have time for aimless AI

Tractable doesn't have time for aimless AI

Amongst a wave of buzzy deep-learning startups in the early 2010s, Tractable has managed to grow steadily by addressing a very specific problem with the technology: helping insurance companies to automatically assess vehicle damage.

Founded by Alexandre Dalyac and Razvan Ranca in 2014 after graduating from the company builder programme at Entrepreneur First (EF), Adrien Cohen joined the company soon after as chief business officer, and set out to discover where to apply its computer vision expertise.

© Tractable
© Tractable

“If you take a step back, at the time there was a wave of deep learning startups,” Cohen told Techworld last week, speaking from a conference room at Tractable’s London office, which is an entire floor of a WeWork near Old Street station in east London. “There was a big number of companies with great technology, but no problem to solve. Most of these companies, they ended up being acquired.”

One example is Magic Pony, the image recognition startup which also graduated from the 2014 EF cohort and was acquired by Twitter in 2015 for $150 million. Then there is Bloomsbury AI, another EF alumni which was purchased by Facebook in 2018, and MetaMind, the AI specialist acquired by Salesforce in 2014 and whose founder Richard Socher is now the chief scientist at the Californian cloud firm.

Read next: UK insurtech startups to watch

Tractable, on the other hand, had a narrower goal. It just didn’t know what it was yet.

“We looked at pipeline inspection, we looked at medical imagery – which has been in a big application of deep learning – we looked at oil and gas, seismic interpretations; anything that was visual,” Cohen explained. “We eventually ended up looking at car accidents, because we realised it was the perfect narrow task for AI.”

That doesn’t mean it was simple, however: “Predicting the way you’re going to repair a car is extremely complex. You have external damage, internal damage, you have so many makes and models, the lighting, the car park, it’s extremely complex.

“It had never been done. So by definition, clearly, the machine learning team was like: ‘well, that looks like a very hard problem to solve’. But we started working on it, and we managed to solve it.”

The stack

What the firm actually built over the ensuing four years was a sophisticated image recognition application that can assess the damage level to a vehicle just from a set of smartphone photographs.

This involved developing a set of neural network models on a common set of cloud tools and services, eventually running them on cloud GPU servers, predominantly with the vendor Amazon Web Services (AWS).

“You need the data, you need the domain knowledge to know how to use this data and train your system and then you need a set of machine learning techniques that are specific to the complexity of the challenge we’re facing,” Cohen said.


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