Injection molding process time reduced by 28%... Field experience translates into external supply contracts.
Third-quarter robot-related revenue reached approximately 2.7 billion KRW, accounting for about 8% of quarterly sales.
IL (307180) is transforming the robotics operational experience gained from its automotive parts factory into a data sales and subscription business. The company validates robot performance on its own production lines and supplies the accumulated data tailored to the manufacturing environments of its clients. The strategy is to generate recurring revenue through learning data and operational solutions even after the initial hardware sale.
In a written interview with Market Times, IL revealed that more than half of the 220 million KRW robot and data supply contract signed in September was attributed to field validation and artificial intelligence (AI) training data. The company highlighted its operational records and experience in handling unexpected situations in actual mass-production plants as the core competitiveness of its data business.
The company explained that its recent profit growth was driven by improved profitability in automotive electronics and Intelligent Transport Systems (ITS), as well as operational efficiencies across its subsidiaries. The following is a Q&A regarding the company's performance, integration effects, and data monetization plans.
Q. What is the background behind the increase in operating profit and the decline in gross margin for the third quarter?
A. The cumulative consolidated revenue for the third quarter of 2026 reached 83.5 billion KRW, consistent with the same period last year, but operating profit surged to 5.1 billion KRW from 800 million KRW a year prior. This marks the highest operating profit in the company's history.
The improvement was driven by profitability gains in the automotive electronics and ITS sectors. Focusing on business efficiency and profit-oriented management rather than mere revenue expansion proved effective. The absorption of ITRONICS in December 2025 allowed ITS performance to be fully reflected in 2026, and group-wide efficiency measures, such as the relocation of IL Cellion to Cheonan, began yielding results this year.
For the third quarter alone, the company recorded its highest quarterly revenue of 33.4 billion KRW (up 14.4% year-on-year) and an operating profit of 2.1 billion KRW.
The decline in gross margin is attributed to rising material costs in automotive electronics and one-time costs associated with the initial mass production of silicone lenses. However, since operating profit increased despite the lower gross margin, we estimate that the efficiency of selling, general, and administrative (SG&A) expenses is continuing.
Moving forward, we plan to increase the proportion of solution-based revenue—combining data, control, and operational software with hardware supply—to boost profitability in new businesses.
Q. What has changed following the merger with ITRONICS?
A. IL acquired the remaining stake in ITRONICS for approximately 3.8 billion KRW in September 2025 and completed the absorption merger on December 1 of the same year. ITRONICS is an ITS specialist that has installed Hi-Pass lane control systems at 162 of the 437 highway toll gates nationwide, holding a market share of approximately 37%. The merger simplified the governance structure and resolved concerns regarding the dilution of parent company value due to overlapping structures.
Technologically, we have internalized capabilities for smart cities and autonomous driving infrastructure by combining ITRONICS' C-ITS and V2X technology with IL's silicone lens optical technology.
Q. Why was IL Mobility made a wholly-owned subsidiary?
A. On September 21, 2026, we acquired the remaining 610,000 shares of IL Mobility for 10 billion KRW in cash to secure 100% ownership. The goal is to strengthen the foundation for humanoid industry validation and enhance competitiveness in physical AI operational data.
IL Mobility serves as a hub for validating humanoids in automotive parts injection molding. By making it a wholly-owned subsidiary, we have integrated R&D, production, quality, and operational functions, accelerating decision-making.
Results have already emerged. In a two-month validation of the 'IL-Bot' at the IL Mobility injection line, average cycle time was reduced by 28%, from 30.0 seconds to 21.6 seconds. Work deviation was reduced by 88.5%, post-injection waiting time by 92%, and daily throughput increased from approximately 1,200 to over 1,600 units.
This validation experience led to the September contract with an automotive parts manufacturer. We plan to build a humanoid smart factory on the 7,341-pyeong site in Hwaseong, Gyeonggi Province, owned by IL Mobility.
Q. What products and customers generated the robot revenue?
A. The third-quarter results include approximately 2.7 billion KRW in robot-related revenue, including the humanoid IL-Bot. This represents about 8% of the 33.4 billion KRW quarterly revenue, signaling that the humanoid business has moved beyond the R&D and validation stage into actual revenue generation.
Sales are expanding into manufacturing, logistics, distribution, and entertainment. We are currently supplying the IL-Bot C2 (semi-humanoid), Y1 (commercial/service), and L1 (industrial quadruped).
Q. What are the competitiveness and monetization plans for PHYDATUM?
A. PHYDATUM, unveiled on October 26, is a physical AI operational data platform. It collects data on tasks, movement, status, and error recovery in real-time, standardizes it, and applies it back to robots after AI training. The more the robot works, the more data is accumulated, which in turn enhances the robot's intelligence.
Unlike startups that rely on virtual simulations, IL accumulates data in actual mass-production environments like the Hwaseong injection plant. We believe that while hardware can be purchased by anyone, field operational data and experience in handling anomalies are difficult to replicate. Regarding monetization, we are introducing standalone data sales and subscription-based models, where pricing varies based on the precision of the training data.
