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Poor or uncategorized raw data can be a major impediment for enterprises that want to build high-quality artificial intelligence that has a meaningful impact on their business. Organizing unstructured data such as images and audio can present a particularly daunting obstacle in this regard.
Today, Paris-based Kili Technology unveiled its service that allows enterprises to annotate raw data such as video, drone aerial images, contracts, and emails. The company’s collaborative platform enables employees to make the data labeling process more efficient.
The company also said it had raised its first outside funding in a round led by Serena Capital and e.ventures, which invested along with business angels such as Datadog CEO Olivier Pomel, Algolia CEO Nicolas Dessaigne, and PeopleDoc founders Stanislas de Bentzmann and Gus Robertson. After a fast start, the company has ambitious plans to expand its international reach.
“The mission is super simple,” said Kili CEO and cofounder François-Xavier Leduc. “To build AI, you need three things. You need the computing power that you can buy easily on Amazon, you need an algorithm that is available as open source, and you need training sets. We are making the bridge between the raw data and what is required to build AI at scale for companies. Our mission is to help our customers turn this raw data into training data so that they can scale AI applications on their internal challenges to solve their issues.”
The company is part of a fast-moving and competitive data annotation sector. Dataloop last year raised $16 million for its data annotation tools. SuperAnnotate raised $3 million for its AI techniques that speed up data labeling. And earlier last year, IBM released annotation tools that tap AI to label images.
All of these companies have identified similar issues with developing high-quality AI: Getting data that can be readily processed to train AI. According to Kili, 29,000 Gigabytes of unstructured data are published every second, but much of it remains useless when it comes to training AI.
Founded in 2018 by Leduc and CTO Édouard d’Archimbaud, Kili offers a stable of experts to complement a company’s internal teams and help accelerate the annotation process.
Kili builds on work d’Archimbaud did while at BNP Paribas, where he ran the bank’s artificial intelligence lab. His team was trying to build models for processing unstructured data and ended up creating their own tools for data annotation.
Kili’s system, as d’Archimbaud explained, relies on a basic concept, similar to tagging people in a photo on Facebook. When users click on an image, a little box pops up so they can type in a name and attach a label to the image. Kili uses AI to allow enterprises to take this process to an industrialized scale to create higher-quality datasets.
“Before, people were thinking that AI was about algorithms, and having the most state-of-the-art algorithm,” d’Archimbaud said. “But it’s not the case anymore. Today, AI is about having the best data to train models.”
Kili’s cofounders bootstrapped the company for its first two years. But Kili has already attracted large customers in Europe, China, and the U.S. across a variety of industries.
As Kili gained more traction, the confounders decided to raise their first outside round of funding to accelerate sales and marketing. But they also intentionally sought out business angels who worked in other data-related startups to help provide practical guidance on building a global company to seize a growing opportunity.
“Two years ago, the data annotations market was estimated to be $2 billion in four years,” Leduc said. “And now it’s estimated to be $4 billion. It’s going to go fast, and it will definitely be huge. And it’s a new category. So there is an opportunity to be a worldwide leader. Today, we are positioned to be one of them.”
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