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From Defense to Shipbuilding… NC AI Sets Out to Develop Autonomous Welding AI for Hanwha Ocean

Next-Generation VLM 'Backy Vision' Implemented Automation from Weld Seam Detection to Robot Control Hanwha Ocean Moves to Apply Construction Processes for Special-Purpose and Commercial Vessels Expectations for Productivity Innovation in the Shipbuilding Industry

Kim Hyun-ah
2026-06-04 09:09:48
[Edaily Reporter Kim Hyun-ah] NC AI, a domestic physical AI specialist, is making a full-scale entry into the market for the automation and automation of welding processes—a key challenge in the shipbuilding industry.

Building on its robot AI expertise accumulated in the defense and manufacturing sectors, the company is expanding its business scope to include the shipbuilding and marine industries.

NC AI, a subsidiary of game company #NC, announced on the 4th that it has secured a contract from #Hanwha Ocean for the “Development of Vision-Based Dedicated Welding Models and Collaborative Robot-Based Autonomous Welding Models” and has commenced full-scale joint development.

The goal of this project is to integrate artificial intelligence (AI) and robotics technology into welding operations—a core process in shipbuilding that has traditionally relied on the experience and know-how of skilled workers.



The vision is to implement an autonomous welding system that goes beyond the level of existing automation—which simply repeats pre-programmed paths—by enabling robots to independently recognize and assess welding areas to perform optimal operations.

In particular, shipyard welding sites are considered challenging environments for applying AI vision technology due to intense arc light, sparks, welding fumes, and lens contamination. NC AI is advancing its technology by refining AI models based on data collected from Hanwha Ocean’s actual work sites and incorporating real-time feedback from on-site engineers.

The company plans to secure “shipbuilding-specific vision recognition technology” capable of precisely identifying weld lines and detecting defects in real time, even in high-noise environments. The developed autonomous welding system is expected to be applied to Hanwha Ocean’s commercial and special-purpose vessel construction processes in the future, contributing to improved productivity and quality.

Next-Generation VLM 'VAETKI Vision' at the Core
NC AI is applying its proprietary industry-specific Vision-Language Model (VLM), 'VAETKI Vision,' as the core technology for this project.

VAETKI Vision is a multimodal AI model capable of simultaneously understanding visual information, such as images and videos, and text commands. NC AI plans to evolve this into a VLA (Vision-Language-Action) model that integrates visual, linguistic, and behavioral processing, enabling it to control the robot’s actual movements.

This will allow operators to control robots using only voice or text commands, without the need for complex programming. The system works by having the AI analyze the welding target and work environment in real time to autonomously determine and execute the optimal welding path, torch angle, and speed.

Expanding Beyond Defense and Manufacturing into Shipbuilding
NC AI is expanding its industry-focused physical AI business, recently undertaking a multi-robot control project in the defense sector with Hyundai Rotem and pursuing robot AI technology cooperation with POSCO DX.

By securing this Hanwha Ocean project, the company has broadened its business scope beyond defense and manufacturing to include the shipbuilding and marine industries, thereby strengthening its position as an industry-specialized AI firm.

Lee Yeon-soo, CEO of NC AI, stated, “Our collaboration with Hanwha Ocean, a leading player in South Korea’s shipbuilding industry, is a crucial opportunity to demonstrate the scalability of ‘Baeki,’ our industry-specific AI foundation model.” He added, “We will develop vision recognition technology and autonomous control models that operate stably even in harsh shipyard environments to deliver world-class physical AI that can be applied to actual production sites.”

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