Most organizations say they aren’t absolutely ready to make use of generative AI in a secure and accountable method, in response to a current McKinsey report. One concern is explainability – understanding how and why AI makes sure choices. Whereas 40% of respondents view it as a big danger, solely 17% are actively addressing it, per the report.
Seoul-based Datumo started as an AI information labeling firm and now desires to assist companies construct safer AI with instruments and information that allow testing, monitoring, and enhancing their fashions—with out requiring technical experience. On Monday the startup raised $15.5 million, which brings its complete raised to roughly $28 million, from traders together with Salesforce Ventures, KB Funding, and SBI Funding, amongst others.
David Kim, CEO of Datumo and a former AI researcher at Korea’s Company for Defence Growth, was pissed off by the time-consuming nature of information labeling so he got here up with a brand new concept: a reward-based app that lets anybody label information of their spare time and earn cash. The startup validated the thought at a startup competitors at KAIST (Korea Superior Institute of Science and Know-how). Kim co-founded Datumo, previously often known as SelectStar, alongside 5 KAIST alumni in 2018.
Even earlier than the app was absolutely constructed, Datumo secured tens of hundreds of {dollars} in pre-contract gross sales throughout the buyer discovery section of the competitors, principally 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. Right this moment, the startup counts main Korean corporations like Samsung, Samsung SDS, LG Electronics, LG CNS, Hyundai, Naver, and Seoul-based telecom large SK Telecom amongst its shoppers. A number of years in the past, nevertheless, shoppers started asking the corporate to transcend easy information labeling. The seven-year-old startup now has greater than 300 shoppers in South Korea and generated about $6 million in income in 2024.
“They wished us to attain their AI mannequin outputs or examine them to different outputs,” Michael Hwang, co-founder of Datumo, instructed TechCrunch. “That’s once 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 centered on AI belief and security, Hwang added.
“We began in information annotation, then expanded into pretraining datasets and analysis because the LLM ecosystem matured,” Kim instructed TechCrunch.
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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 providers. The Meta deal additionally indicators that competitors for AI coaching information is intensifying.
Datumo shares some similarities with corporations like Scale AI in pretraining dataset provisioning, and with Galileo and Arize AI in AI analysis and monitoring. Nevertheless, it differentiates itself by means of its licensed datasets, significantly information crawled from revealed books, which the corporate says gives wealthy structured human reasoning however is notoriously tough to scrub, in response to CEO Kim.
Not like its friends, Datumo additionally gives a full-stack analysis platform referred to as Datumo Eval, which robotically generates take a look at information and evaluations to examine 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 gentle dedication. The whole funding course of took about eight months, Hwang mentioned.
The brand new funding might be used to speed up R&D efforts, significantly in creating automated analysis instruments for enterprise AI, and to scale world go-to-market operations throughout South Korea, Japan, and the U.S. The startup, which has 150 workers in Seoul, additionally established a presence in Silicon Valley in March.
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