Most organizations say they aren’t totally ready to make use of generative AI in a protected and accountable manner, in line with a current McKinsey report. One concern is explainability — understanding how and why AI makes sure selections. Whereas 40% of respondents view it as a major danger, solely 17% are actively addressing it, per the report.
Seoul-based Datumo started as an AI knowledge labeling firm and now needs to assist companies construct safer AI with instruments and knowledge that allow testing, monitoring, and bettering their fashions — with out requiring technical experience. On Monday the startup raised $15.5 million, which brings its whole raised to roughly $28 million, from traders together with Salesforce Ventures, KB Funding, ACVC Companions, and SBI Funding, amongst others.
David Kim, CEO of Datumo and a former AI researcher at Korea’s Company for Protection Growth, was annoyed by the time-consuming nature of information labeling so he got here up with a brand new thought: a reward-based app that lets anybody label knowledge of their spare time and earn cash. The startup validated the concept at a startup competitors at KAIST (Korea Superior Institute of Science and Expertise). Kim co-founded Datumo, previously referred to as SelectStar, alongside 5 KAIST alumni in 2018.
Even earlier than the app was totally constructed, Datumo secured tens of 1000’s of {dollars} in pre-contract gross sales in the course of the buyer discovery section of the competitors, largely from KAIST alumni-led companies and startups.
In its first yr, the startup surpassed $1 million in income and secured a number of key contracts. At the moment, the startup counts main Korean firms like Samsung, Samsung SDS, LG Electronics, LG CNS, Hyundai, Naver, and Seoul-based telecom large SK Telecom amongst its purchasers. A number of years in the past, nevertheless, purchasers started asking the corporate to transcend easy knowledge labeling. The 7-year-old startup now has greater than 300 purchasers in South Korea and generated about $6 million in income in 2024.
“They needed us to attain their AI mannequin outputs or examine them to different outputs,” Michael Hwang, co-founder of Datumo, instructed iinfoai. “That’s after we realized: We have been already doing AI mannequin analysis — with out even understanding it.” Datumo doubled down on this space and launched Korea’s first benchmark dataset targeted on AI belief and security, Hwang added.
“We began in knowledge annotation, then expanded into pretraining datasets and analysis because the LLM ecosystem matured,” Kim instructed iinfoai.
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Meta’s current $14.3 billion acquisition-like funding in data-labeling firm Scale AI highlights the significance of this market. Shortly after that deal, AI mannequin maker and Meta competitor OpenAI stopped utilizing Scale AI’s companies. The Meta deal additionally alerts that competitors for AI coaching knowledge is intensifying.
Datumo shares some similarities with firms like Scale AI in pretraining dataset provisioning, and with Galileo and Arize AI in AI analysis and monitoring. Nonetheless, it differentiates itself by means of its licensed datasets, notably knowledge crawled from revealed books, which the corporate says provides wealthy structured human reasoning however is notoriously tough to scrub, in line with CEO Kim.
Not like its friends, Datumo additionally provides a full-stack analysis platform known as Datumo Eval, which mechanically generates take a look at knowledge and evaluations to test for unsafe, biased or incorrect responses with out the necessity for handbook scripting, Kim added. The signature product is a no-code analysis software designed for non-developers like these on coverage, belief and security, and compliance groups.
When requested about attracting traders like Salesforce Ventures, Kim defined that the startup had beforehand hosted a fireplace chat with Andrew Ng, founding father of DeepLearning.AI, at an occasion in South Korea. After the occasion, Kim shared the session on LinkedIn, which caught the eye of Salesforce Ventures. Following a number of conferences and Zoom calls, the traders prolonged a mushy dedication. Your complete funding course of took about eight months, Hwang stated.
The brand new funding will probably be used to speed up R&D efforts, notably in growing automated analysis instruments for enterprise AI, and to scale international go-to-market operations throughout South Korea, Japan, and the U.S. The startup, which has 150 staff in Seoul, additionally established a presence in Silicon Valley in March.
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