Trusted AI Systems For Enterprise Deployment
Production-grade architecture, structured data pipelines, and governance frameworks designed for secure, scalable AI adoption across enterprise and public sector environments.
Why Data Matters
The Quality of Data Determines the Quality of AI
Advanced AI systems fail without high-quality, structured data.
The most sophisticated models are only as reliable as the data they learn from.
Inaccurate, biased, or disorganized data leads to unstable and unpredictable outcomes.
Trust in AI begins with data design
- Trustworthiness
- Explainability
- Reproducibility
- Legal & Ethical Compliance
- DATA
End-to-End AI Data Expertise
EBIT designs, builds, and validates data tailored to AI systems.
AI System Design
Designing AI systems aligned with your business needs
Knowledge Structuring
Organizing knowledge and data for accurate AI understanding
Data Engineering
Building and managing high-quality data at scale
Evaluation & Reliability
Ensuring reliable and trustworthy AI performance
End-to-End AI Data Expertise
A structured approach to building, validating, and deploying reliable AI systems
Model Training & Alignment Layer
SFT (Supervised Fine-Tuning)
Expert-labeled datasets for task-specific model training.
RLHF (Reinforcement Learning from Human Feedback)
Preference data to align model outputs with human expectations.
News
Latest News from EBIT
- Daily Economy2026-03-06
[CEO Story] Yonghan Park, CEO of EBIT: “The Future of AI Lies in Data Verification”
Artificial intelligence (AI) is advancing at a remarkable pace, reshaping the industrial landscape across the globe. Yet behind the dazzling progress of the technology, few people possess deep insight into what AI actually learns and how the outputs it produces are verified. When we met Yonghan Park, CEO of EBIT, at his office, he described himself both as a “trainer” who teaches AI and as an “auditor” who guarantees the safety and reliability of AI systems, presenting a clear vision for the future of the AI era.From Finance Professional to Global AI Data PartnerPark’s path differs from that of many typical IT entrepreneurs. Originally a finance professional who studied business administration and economics at Boston College in the United States, he founded the company EBIT about seven years ago with the goal of building a stock investment community platform.Although the first business ultimately failed after confronting the harsh realities of the market, it instead became a stepping stone toward a much larger opportunity.Through connections he had built during his time studying in the United States, Park gained the opportunity to participate as an Asian partner of Scale AI, a global leader in AI data infrastructure. This turning point transformed EBIT from a simple startup into a thoroughly global-oriented company, with almost all of its revenue now generated overseas.Today, EBIT plays a critical role in refining and verifying training data for AI models being developed by global tech companies such as OpenAI’s ChatGPT and Google’s Gemini.“AI was created to automate human work,” Park said. “Ironically, building the foundation that makes this possible requires an extraordinary level of human resources.”He describes EBIT as a collective of “data specialists” responsible for building the massive pyramid that supports modern artificial intelligence.High-Value Data Engineering Beyond Simple LabelingWhen people hear the term “data labeling,” they often imagine low-skilled repetitive tasks, such as drawing boxes around objects in images or performing simple classifications. However, the projects led by Park operate on a completely different level. At EBIT, the work is carried out not by general task workers but by highly educated specialists, including software developers, mathematics graduates, and language experts.The work they perform goes far beyond simple repetitive tasks. Instead, it involves high-value data engineering that enables AI systems to develop mathematical reasoning capabilities or learn complex coding logic.Because such tasks require deep expertise, EBIT provides top-tier compensation to experienced developers while strictly managing the quality of the data.Park emphasizes that “data is the single most critical element that determines the quality of AI.” While semiconductors and electricity serve as the infrastructure supporting the external growth of the AI industry, he believes that data forms the very essence that shapes the intelligence of AI.The Key to Solving AI Hallucinations: Precise Human VerificationPark also offers a sharp diagnosis of hallucination, one of the most widely discussed weaknesses of generative AI today. According to him, the essence of hallucination ultimately lies in “errors in data” and “the absence of verification.”AI models operate as “black boxes,” where the internal causal processes leading to an output are difficult for humans to fully understand. Therefore, the system cannot be considered complete without a process in which human experts directly test tens of thousands of possible scenarios, carefully record the outcomes, and verify the reliability of the results.Within this context, Park predicts that specialized data companies will eventually hold a position similar to that of accountants in the capital markets. Just as publicly listed companies undergo external audits to ensure the transparency of their financial statements, he believes that the AI services adopted by companies will also need to be rigorously verified by expert groups to ensure that they are not socially harmful and do not pose risks such as personal data leakage.Ultimately, reinforcement learning with human feedback (RLHF) will become a core process that aligns a model’s responses with human values and expectations, thereby providing the technological foundation for social trust in AI.Global Achievements and EBIT’s Unique Competitive StrengthSince its founding, EBIT has achieved remarkable results in the global data market within a relatively short period of time. The company has successfully completed large-scale multilingual data construction projects as well as coding data projects for next-generation AI agents, establishing itself as a trusted partner for global big tech companies.Park highlights three key elements that differentiate EBIT.The first is operational know-how accumulated through direct experience with the quality standards required on the front lines of the global AI industry. The second is the creation of a platform-based workforce structure that addresses the fragmented nature of project-based labor, enabling skilled AI trainers to continuously build their careers while deepening their expertise. The third is the company’s ability to provide customized solutions that closely support every stage of the process, from guideline design to final quality inspection, according to the specific characteristics of each client’s AI model.Korea as One of the Most Attractive Strategic Bases for AIFrom a global perspective, Park believes that Korea holds enormous potential in the AI industry. Aside from the United States and China, which possess immense capital resources, few countries have an AI infrastructure as complete as Korea.Korea is one of the rare countries capable of addressing within its own borders all the essential components required for the AI ecosystem, including semiconductor manufacturing capabilities, highly skilled data professionals, stable energy supply systems, and ultra-fast communication networks.In particular, the infrastructure for clean water and stable electricity, both essential for semiconductor production, ranks among the best in the world. Park also notes that the meticulousness and sense of responsibility often associated with Korean professionals serve as critical assets that significantly improve data quality.To expand these strengths globally, EBIT has established corporate entities in Singapore and Japan and strategically manages talent across Asia. By taking into account each country’s cultural background and linguistic characteristics, the company aims to produce the most optimized data outcomes.A Future Where Humanity and AI Coexist, Built on DataIn building his organization, Park prioritizes individuals who can define the essence of complex problems and design concrete solutions to address them, rather than simply those who possess extensive knowledge.His philosophy of boldly eliminating “work for the sake of work” and maximizing efficiency has become the driving force behind EBIT’s ability to create a strong global impact with a small but elite team.Park’s ultimate goal is to go beyond being merely a data supplier and evolve into a global data solutions company that designs and operates all the data infrastructure required for AI models before they are released into the world.As the era of full automation approaches, Park believes that the data forming the foundation of these systems must ultimately be rooted in the most reliable intelligence available, human intelligence.His ambition is clear: to ensure that when people think of “data experts” on the global stage, EBIT is the first name that comes to mind. And that vision is steadily becoming a reality.
- Money Today2026-01-23
EBIT Selected as Supplier for Korea’s 2026 Data Voucher Support Program
EBIT, an AI training data specialist led by CEO Yonghan Park, announced on January 23 that it has been selected as an official data supplier for the 2026 Data Voucher Support Program, a government initiative overseen by the Ministry of Science and ICT and operated by the Korea Data Agency (K-DATA).The Data Voucher Support Program is designed to help small and medium-sized enterprises, startups, and research institutions develop data-driven services and products by subsidizing the cost of data acquisition and processing. Through this program, EBIT plans to provide advanced AI training datasets focused on mathematics and high-level coding to support the development of next-generation large language models (LLMs) and domain-specific AI systems.EBIT’s datasets consist of high-difficulty training data covering mathematical reasoning, algorithmic thinking, and problem-solving-oriented code generation and analysis. Rather than simple question-and-answer formats, the data emphasizes multi-step reasoning and logical inference. According to the company, each dataset includes structured problem-solving processes and step-by-step reasoning paths, enabling AI models to generate more stable and explainable outputs.The datasets are built in standardized formats and categorized by difficulty and task type, with multi-stage quality assurance processes in place. This allows client organizations to immediately apply the data to model training and evaluation while significantly reducing the time and cost required for data design and preprocessing.A company representative stated that EBIT possesses the capability to expand beyond mathematics and coding into industry and professional domains such as finance, business, science and technology, and general knowledge. The company also offers customized datasets tailored to specific needs, including financial calculations, risk analysis, quantitative decision-making, and domain-specific reasoning.Leveraging its experience in AI data construction projects for global technology companies, EBIT has recently expanded collaborations with domestic LLM developers and AI startups. The company noted that its experience in building multilingual, high-complexity reasoning datasets has enabled it to accumulate global-level data quality standards and operational expertise.CEO Yonghan Park said, “We aim to help SMEs, startups, and research institutions efficiently utilize high-quality mathematics and coding-based AI training data,” adding, “By delivering scalable data construction capabilities across diverse industry domains, we will support our clients in enhancing AI model performance and strengthening their R&D competitiveness.”
- Money Today2025-08-29
EBIT Achieves 3 Billion KRW in Cumulative Revenue through Collaboration with Scale AI
Seoul, South Korea — EBIT Co., Ltd. (CEO Yonghan Park), an AI-powered workforce matching platform, announced on the 29th that it has reached cumulative revenue of 3 billion KRW through its collaboration with Scale AI, a global leader in AI training data.EBIT entered into an official partnership with Scale in July 2024. Since then, the company has built a strong track record by sourcing and training workers, managing projects, and delivering diverse domain data across language, speech, coding, and mathematics.The company has also expanded its advanced talent pool through industry-academia partnerships with leading universities and research institutions both in Korea and abroad. Based on this foundation, EBIT has been providing differentiated, high-quality data processing services in highly specialized fields, strengthening its foothold in the global market. “We are currently expanding into partnerships with world-class research institutes and AI enterprises abroad, with multiple global partnerships in progress,” an EBIT spokesperson commented.CEO Yonghan Park stated:“As the AI industry advances, the single most important factor is data quality. By leveraging our specialized talent pool and systematic platform, EBIT will continue to grow alongside global enterprises. This revenue milestone also demonstrates that talent from Korea and across Asia is playing a crucial role at the forefront of global AI innovation.”Looking ahead, EBIT plans to strengthen its partnerships further to meet the growing global demand for AI data, while continuing to contribute to the advancement of the AI industry through the provision of high-quality data.
Testimonials
Trusted by Leading Enterprises
Coding Agent / Evaluation
“Building reliable coding agents requires more than scale. It requires data grounded in real-world environments. EBIT delivered terminal-based datasets that improved robustness.”
Mich*** McCor****
S***l
Multilingual / APAC
“Scaling multilingual AI across diverse markets is complex. EBIT enabled consistent quality across languages while maintaining speed and operational efficiency at scale.”
Dani*** Lop***
S***e
Enterprise AI Deployment Starts With Structured Data
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