How we built an AI unicorn in 6 years

(This piece was originally published by TechCrunch and is republished here with kind permission.)

Build upon a fresh technological breakthrough

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”

Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.

The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.

Search for complementary co-founders who will become your best friends

I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.

But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion.

Adrien would teach us how to build a business, inspire trust and hire world-class talent.

Find potential customers early so you can work out market fit

Tractable started at EF with a head start — a paying customer. Our first use case was… plastic pipe welds.

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.

And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.

We struck gold with car insurance. A huge and inefficient market in desperate need of modernization. What if, after a car accident, people could take photos of the damage with their phone and let the AI handle their insurance claim automatically, without hassle?

Generate FOMO to raise funding

Somehow, we raised $1.9 million. It started with EF’s CEO, Matt Clifford, saying one day, “Someone’s coming in to visit, a Google early investor. Win him over and you’ll be set.” That person was Charlie Songhurst. He started off with a $50,000 commitment, but the more American angel friends he introduced, the easier it became to win a $100,000+ commitment from the next.

One of them called me while they were driving, we spoke for 15 minutes, and they committed $300,000. To this day, I have never met this person.

An “angel party round” was great, but Matt thought we needed the disciplined guidance of a venture capital (VC) fund. That would be Ash Fontana, from Zetta Venture Partners in San Francisco. He’d seen our pitch video, had calls with us, but did not yet feel ready to make a move.

However, that changed once we had others involved — the FOMO kicked in. After speaking to a couple of insurance prospects and hearing that we were about to sign, he flew from San Francisco to London the next day and wouldn’t leave until we’d let him in to lead the round.

On the big day, we were in Berlin, staying on a boat hostel with no means to print and sign the investment docs with a witness. We had to find a print shop and ask people on the street and ask them: “Can you write your signature here so we can get $2 million for our company.” For some reason, this took a few attempts. But we got there.

The following round, an $8 million Series A, would be much harder because we would end up far short of the $1 million of recurring revenue needed by then. By introducing and following up with investors, Fontana’s support made the difference.

Sign and announce famous customers

Despite the Series A, we didn’t know if we had a business. It had taken 12 months to sign a one-month pilot with an insurer, and the income from it barely covered the travel costs incurred. We even explored getting acquired by a tech giant for $25 million (which, looking back, thankfully didn’t work out).

What changed our trajectory was signing our first million-dollar customer contract. We were able to show an insurer that our AI could potentially generate $50-worth of speed and expense reduction on each of their 1 million claims.

Just a year ago, I had ducked under the table to stifle nervous laughter as Adrien asked a prospect for $30,000/month. Now I was bringing back $1 million of business to feed the Tractable family.

Doing this three times over brought us into the legendary “10x year-on-year growth” club. When it was time to raise the Series B, it was like riding on a warm knife through butter, and the round culminated in being led by top-tier investor Insight Partners.

We also learned that announcing top-tier customers publicly is key to rocket-ship growth in enterprise.

Many large companies will hesitate to adopt a disruptive solution offered by a newcomer, even if the product is best in class. But if you can succeed with one of them and agree to a public announcement, that will be the seismic shift: Now all of their competitors will risk falling behind.

We discovered this FOMO in Japan, where we now work with all of the country’s large insurers. We are replicating it in France, Poland and — most importantly — the U.S., the world’s largest market.

Find your mission

We were proud to be building a fast-growth business centered on cutting-edge AI. However, the team kept asking what our mission was: How we make a meaningful positive difference.

We started paying attention not just to growth, technology and value creation, but also to positive impact. We realized that when crashed cars are too expensive to repair, they’re sold for scrap at online auctions. And our AI could help figure out which ones to recycle.

We now do this with the world’s largest automotive recycler, LKQ. Every car recycled for parts represents about half a metric ton of carbon dioxide emissions avoided. Having our AI analyze photos of damaged cars on auction — and help suggest which parts are in good enough condition to be recycled — improves the process and makes recycling cars more valuable.

We also realized that we could help people recover faster from natural disasters by building AI for appraising damage to homes. One of the worst things about climate change is the increase in frequency and severity of natural disasters like typhoons and hurricanes. These extreme events will wreck homes, leaving thousands of people without a proper roof over their heads. This autumn, we hope our AI for home damage appraisal will help a thousand Japanese families rebuild their homes faster.

However, not everyone can afford home insurance. In partnership with our investors at Georgian, we’re looking to create an AI disaster recovery fund to deploy our AI to those who need it most, regardless of whether they can pay. Our source of funding is rather original: In the earliest days of Tractable, a brilliant intern used free compute credits and idle servers to mine Ethereum during the cryptocurrency’s first days of existence. After holding it for six years, this is worth millions and ready for a greater purpose.

Looking back, I feel lucky that we were at the vanguard of the AI revolution. I’m thankful to the art of FOMO to raise funds and drive customer adoption.

I’m glad that we didn’t end up selling out early.

They say the first billion is the hardest, and right now it really does feel that way. We want to push the limits further, become the company that puts a visual expert in people’s pocket to help you get your car, home (and more!) sorted out, anytime, hassle free. I hope we get there.

(image credit: Getty Images/TechCrunch)