Labeling Platform for High-Quality Training Data. One tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI. ___ Why Kili Technology? You might not know this, but: MNIST’s dataset has an error rate of 3.4% and is still cited by more than 38,000 papers. The ImageNet dataset, with its crowdsourced labels, has an error rate of 6%. This dataset arguably underpins the most popular image recognition systems developed by Google and Facebook. Systemic error in these datasets has real-world consequences. Models trained on error-containing data are forced to learn those errors, leading to false predictions or a need of retraining on ever-increasing amounts of data to “wash out” the errors. Every industry has begun to understand the transformative potential of AI and invest. But the revolution of ML transformers and relentless focus on ML model optimization is reaching the point of diminishing returns. What else is there? ______ The Company Kili began as an idea in 2018. Edouard d’Archimbaud, our co-founder and CTO, was working at BNP Paribas, where he built one of the most advanced AI Labs in Europe from scratch. François-Xavier Leduc, our co-founder and CEO, knew how to take a powerful insight and build a company around it.While all the AI hype was on the models, they focused on helping people understand what was truly important: the data. Together, they founded Kili Technology to ensure data was no longer a barrier to good AI.By July 2020, the Kili Technology platform was live and by the end of the year, the first customers had renewed their contract, and the pipeline was full. In 2021, Kili Technology raised over $30M from Serena, Headline and Balderton. Today Kili Technology continues its journey to enable businesses around the world to build trustworthy AI with high-quality data.