KT has completed a companywide data integration project for Amorepacific's Research & Innovation Center, delivering an AI-ready data platform that consolidates 70 years of R&D data. The project, called Data Highway, reorganized legacy research assets so they can be queried in natural language, giving researchers faster access to ingredient data, formulations, experimental results, and reports.
Turning seven decades of research into queryable data
Amorepacific's R&D data spanned structured and unstructured formats across multiple systems. KT's Data Highway standardizes that information into a centralized repository built for continuous accumulation and management. The platform serves as a common data foundation for future AI services and agents, supporting both current retrieval needs and downstream automation.
The integration means researchers no longer need to navigate siloed databases. Instead, they can ask questions in plain language and receive relevant answers pulled from the entire corpus of institutional knowledge. This approach is a practical example of AI for Science & Research applied to a consumer goods lab environment.
Lemon: an AI assistant for lab productivity
Alongside the data platform, KT and Amorepacific developed Lemon (Lab Efficiency Mode ON), an AI assistant tailored to R&D workflows. Lemon processes natural language queries against the integrated data, analyzing ingredient properties, formulation histories, test outcomes, and research reports to surface relevant answers. The goal is to cut the time researchers spend hunting for information, freeing them to focus on product innovation.
KT's approach to building the assistant avoided a one-off tool; it sits on top of the unified data layer, meaning its accuracy and scope can improve as more data is added and as the platform supports more autonomous AI agents in the future.
Partnership and enterprise expansion plans
KT won the contract in December last year and has been working with Amorepacific since then to establish a strategic AI infrastructure for R&D. The two companies plan to expand the AI-ready environment and support a broader range of AI services and autonomous agents. KT's Enterprise Business head, Noh Hyung-rae, said, "We will leverage our experience from the project to expand AI-ready data-based AI transformation initiatives across industries including manufacturing, retail and services, while supporting corporate AI adoption."
Why this matters for IT and Development
For teams building internal data platforms, the Data Highway project offers a concrete reference for turning decades of messy, unstructured institutional knowledge into an AI-ready corpus. It shows how to combine data standardization, natural language querying, and a common data layer that can serve both current assistants and future agentic systems. The pattern applies broadly: before deploying AI tools, organizations need to invest in making their data machine-readable and queryable at scale. This project also highlights the growing demand for IT and development professionals who can bridge legacy data systems and modern AI pipelines, a skill set central to AI for IT & Development.
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