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§ Private Profile · San Francisco, CA, USA
Data annotation platform providing crowdsourced data labeling for training AI and machine learning models, focused on self-serve capabilities.
Figure Eight has raised $79.2M across 8 funding rounds.
Key people at Figure Eight.
Figure Eight has raised $79.2M in total across 8 funding rounds.
Figure Eight provided an enterprise technology platform utilizing crowdsourced human labor to annotate and structure raw data for training artificial intelligence and machine learning models. The organization focused on delivering self-serve data annotation capabilities that supported complex algorithmic tasks across multiple industries, such as geographic mapping, stock photography analysis, and automated receipt scanning for corporate expense reports. In March 2019, the business was acquired by Appen in an all-cash transaction valued at up to $300 million, which consisted of a $175 million upfront payment alongside up to $125 million in performance-based earnouts. This strategic acquisition, executed under the direction of Appen chief executive officer Mark Brayan, provided a successful exit for early institutional backers, including Canvas venture capitalist Rebecca Lynn. Figure Eight was originally founded in 2008 by technology entrepreneurs Robin Bordoli and Lukas Biewald.
Figure Eight has raised $79.2M in total across 8 funding rounds.
Figure Eight's investors include Jonathan Roosevelt, Canvas Ventures, M12, John Somorjai, Trinity Ventures, Industry Ventures, Andreessen Horowitz, DST Global, Felicis Ventures, March Capital, Matrix, Openview Venture Partners.
Figure Eight has raised $79.2M across 8 funding rounds. Most recently, it raised $20.0M Other Equity in June 2017.
Key people at Figure Eight.
Figure Eight was a pioneering technology company that built a human-in-the-loop AI platform for data science and machine learning teams, specializing in generating high-quality training data from text, images, audio, and video.[1][2][3] It served AI/ML developers and enterprises in sectors like autonomous vehicles, intelligent assistants, medical imaging, content moderation, and customer support, solving the critical bottleneck of poor training data quality that hinders real-world ML deployment by combining human intelligence with algorithms for labeling, tuning, and validation.[1][4] With over 100 million images labeled, 10 billion human judgments, and a decade of impact, the company raised $55.95M before its 2019 acquisition by Appen for up to $300M, marking strong growth in the data annotation space.[1][2][5]
Figure Eight was founded in 2007 (initially as Dolores Labs) by Lukas Biewald and Chris Van Pelt, who identified a gap for simple, non-automatable tasks like image annotation and text transcription to fuel ML algorithms.[4][5] Starting with experiments on Amazon Mechanical Turk—collecting 20 million face assessments via Facestat in three months—they pivoted to serve companies like Zvents and O'Reilly Media, rebranding to CrowdFlower and later Figure Eight.[4] Early traction came from crowdsourcing human labor for data tasks, evolving into a full platform with automated workflows; pivotal moments included partnerships with giants like Google, Facebook, and Toyota, plus initiatives like the TrainAI conference and $1M AI for Everyone contest for nonprofits.[1][4]
Figure Eight rode the explosive AI training data boom in the 2010s, as ML adoption surged but was bottlenecked by data scarcity—timely amid deep learning's rise post-2012 ImageNet breakthroughs.[1][4] Market forces like autonomous driving (e.g., Toyota) and NLP (e.g., chatbots) favored its platform, influencing the ecosystem by popularizing crowdsourced annotation and setting standards for hybrid human-AI workflows now emulated by competitors like Labelbox and Hive.[2][4] Its acquisition by Appen consolidated the data labeling market, accelerating enterprise AI while highlighting the shift from raw compute to quality data as the new moat.
Post-2019 acquisition, Figure Eight's tech endures within Appen, powering scaled data services amid generative AI's data hunger—expect deeper integration with foundation models and edge cases like multimodal training.[2][5] Trends like synthetic data and active learning will shape its legacy, potentially evolving influence toward fully automated loops while human oversight remains key for trust and accuracy. This human-AI fusion cements Figure Eight's role as the essential bridge that made real-world ML viable, echoing its founding insight on data over algorithms.[1][4]
