Most organizations say they are not fully prepared to use generative AI in a safe and responsible way, according to a recent McKinsey report. One concern is explainability—understanding how and why AI makes certain decisions. While 40% of respondents view it as a significant risk, only 17% are actively addressing it.
Seoul-based Datumo began as an AI data labeling company and now helps businesses build safer AI with tools and data for testing, monitoring, and improving their models—without requiring technical expertise. The startup recently raised $15.5 million, bringing its total funding to approximately $28 million. Investors include Salesforce Ventures, KB Investment, and SBI Investment.
David Kim, CEO of Datumo and a former AI researcher at Korea’s Agency for Defence Development, was frustrated by the time-consuming nature of data labeling. He developed a reward-based app that lets users label data in their spare time and earn money. The idea was validated at a startup competition at KAIST, where Kim co-founded Datumo, formerly known as SelectStar, with five KAIST alumni in 2018.
Even before the app was fully built, Datumo secured tens of thousands of dollars in pre-contract sales during the competition’s customer discovery phase, mostly from KAIST alumni-led businesses. In its first year, the startup surpassed $1 million in revenue and secured key contracts. Today, its clients include major Korean companies like Samsung, LG Electronics, Hyundai, Naver, and SK Telecom.
Several years ago, clients began asking Datumo to go beyond data labeling. The seven-year-old startup now has over 300 clients in South Korea and generated about $6 million in revenue in 2024.
“They wanted us to score their AI model outputs or compare them to other outputs,” said Michael Hwang, co-founder of Datumo. “That’s when we realized we were already doing AI model evaluation—without even knowing it.” Datumo then focused on this area and released Korea’s first benchmark dataset for AI trust and safety.
“We started in data annotation, then expanded into pretraining datasets and evaluation as the LLM ecosystem matured,” Kim said.
Meta’s recent $14.3 billion investment in data-labeling company Scale AI highlights the importance of this market. Shortly after the deal, OpenAI stopped using Scale AI’s services. The investment also signals intensifying competition for AI training data.
Datumo shares similarities with companies like Scale AI in pretraining datasets and with Galileo and Arize AI in AI evaluation and monitoring. However, it differentiates itself through licensed datasets, including data from published books, which offers structured human reasoning but is difficult to clean.
Unlike its peers, Datumo offers a full-stack evaluation platform called Datumo Eval, which automatically generates test data and evaluations to check for unsafe, biased, or incorrect responses without manual scripting. The no-code tool is designed for non-developers, such as policy, trust and safety, and compliance teams.
When asked about attracting investors like Salesforce Ventures, Kim explained that the startup had hosted a fireside chat with Andrew Ng, founder of DeepLearning.AI, in South Korea. After sharing the session on LinkedIn, Salesforce Ventures took notice. Following several meetings, the investors extended a soft commitment. The funding process took about eight months.
The new funding will accelerate R&D efforts, particularly in developing automated evaluation tools for enterprise AI, and expand global operations in South Korea, Japan, and the U.S. The startup, which has 150 employees in Seoul, also established a presence in Silicon Valley in March.