Although the global manufacturing industry is accelerating the transition to data-driven smart factories, the liquid material processing stage, which is effectively the first step in production, still remains an “unseen area.” With global battery production projected to reach 4 TWh (terawatt-hours) by 2030 on the back of electric vehicle adoption, and mandatory use of recycled plastics expanding, mainly in the EU, the difficulty of managing raw material quality continues to rise.

Manufacturing complexity is increasing, yet visibility into the process itself remains unchanged. Coating Solution 4U Co., Ltd. has directly targeted this gap.

Founded in February 2023, Coating Solution 4U is a deep-tech startup specializing in AI-based manufacturing solutions, led by Professor Ahn Kyung-hyun of the School of Chemical and Biological Engineering at Seoul National University. Ahn explained the background of the company’s founding, saying, “Variations in the quality of liquid materials handled in the early stages of manufacturing determine the final product quality and productivity, yet there has been no technology to measure them, so the field has long relied on experience and intuition,” and added, “If it cannot be measured, it cannot be controlled, and this ultimately created a structure in which raw material losses and cost burdens were repeated.” After hearing directly from the field and confirming the substance of the problem, Ahn completed a proprietary diagnostic technology through research.

The core is AI-based real-time fluid diagnostic equipment. This all-in-one diagnostic device combines sensors, data, and AI analysis algorithms, collecting the flow state of fluids occurring in processes in millisecond (one-thousandth of a second) units and interpreting it through machine learning.

Because it is a non-invasive, plug-and-play system that does not require replacement of existing equipment, it can be immediately applied on site to detect quality anomalies at an early stage. The diagnostic kit, measuring 357×300×271 mm, is equipped with a touch panel to allow intuitive data visualization. The paradigm has shifted from “post-event discovery,” where the cause is sought after a problem occurs, to “real-time diagnosis,” which identifies anomalies in advance.

This technology is segmented by industry. “SlurryXpert,” which monitors the dispersion state of slurry in battery electrode manufacturing processes with over 95% accuracy, is regarded as the world’s first AI-based inline slurry diagnostic technology. “PlasticXpert,” which diagnoses the state of molten resin in plastic processing and recycling processes, is also at the commercialization stage. As the technology is based on a common manufacturing characteristic of processing raw materials in liquid form, it can be expanded into cosmetics, pharmaceuticals, food, and petrochemicals.

The company’s technological capability has been recognized externally as well. SlurryXpert won the Bronze Award in the Materials Science category at the “2026 Edison Awards” in April. While the Gold and Silver Awards went to large corporate consortia with sales in the trillion-KRW range, this case stood out as a rare example of a startup with relatively small revenue winning purely on technological merit. Fourteen domestic patents registered and filed, six PCT international patents, one U.S. patent application, and one domestic and international trademark also support its technological competitiveness.

The organizational culture is also distinctive. Most of the personnel in management support and marketing are women whose careers had been interrupted by childcare, and a flexible work system has been introduced to allow them to balance work and childcare. Ahn stresses that this approach is enhancing organizational synergy.

The company plans to continue demonstration tests and certification procedures at various sites in the second half of this year and to pursue Series A funding in the first half of next year.

Ahn stated, “The goal is to make invisible liquid processes visible to the eye,” and added, “We hope this technology will be widely deployed and contribute to building ideal manufacturing sites.”

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