What happened
Daewoo Engineering & Construction (E&C) has implemented a series of AI-driven systems across its domestic and international construction sites to manage safety, communication, and administrative workflows. The company is utilizing real-time translation, AI-powered video surveillance, and large language model (LLM) tools to automate site management and risk assessment.
Daewoo E&C has standardized an AI translation system at its domestic sites that converts spoken Korean instructions into text and audio across 16 languages. The company reports a 94.6 percent accuracy rate in Korean speech recognition for this tool, which is paired with a tablet-based platform for worker registration and safety training.
In June, the company deployed an AI-powered smart monitoring system at its G-TOWN project in Gwacheon. This system uses video analysis to detect missing safety equipment, unauthorized personnel in restricted areas, fire hazards, and collapsed workers. The company intends to scale this surveillance and location-tracking technology across its broader operations.
For administrative and quality control, the company is using 'Q-Box,' a platform developed in 2024 that digitizes inspection results. Daewoo claims this system reduced documentation time by over 90 percent during field trials by automating submissions to the national Construction Safety Information System.
Internationally, the company has developed 'Barodap AI,' an LLM-based tool designed to review bidding documents and construction contracts. The tool extracts payment terms, compares contract provisions, and identifies potential risks related to project delays.
Source details: koreaherald.com
Why it matters
The integration of AI into heavy industry represents a shift toward digitizing high-risk environments to mitigate human error and communication gaps. By automating safety monitoring and contract reviews, Daewoo E&C aims to reduce workplace accidents and administrative overhead. These deployments demonstrate how specialized AI applications—ranging from multilingual safety training to automated document analysis—can be applied to complex, multi-stakeholder construction projects to improve operational efficiency and regulatory compliance.
The construction industry faces significant challenges regarding labor safety and complex, multi-jurisdictional contract management. By deploying AI to bridge language barriers, Daewoo E&C addresses a critical safety risk where non-native speakers may misunderstand site-specific safety protocols.
The use of automated hazard detection via provides a proactive safety layer that does not rely on constant human supervision, potentially reducing response times during emergencies.
The automation of contract reviews via LLMs represents a practical application of in the legal and project management sectors, allowing firms to identify contractual risks earlier in the bidding process, which is essential for maintaining profitability in large-scale infrastructure projects.
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What to watch next
The company’s ongoing 'Hyper Safety & AI Open Innovation' program, which is currently evaluating nine external technologies for potential deployment at active sites. Observers should monitor whether these pilot programs successfully transition into long-term, scalable infrastructure across Daewoo's global operations, particularly regarding the efficacy of their proprietary 'Barodap AI' in managing complex international legal disputes.
The effectiveness of the 'Barodap AI' tool in real-world international contract disputes remains to be seen, as the company plans to expand its scope to include blueprint reviews.
The 'Hyper Safety & AI Open Innovation' program, conducted with the Korea Institute of Civil Engineering and Building Technology, serves as a pipeline for future AI integration. The success of the nine finalists in their upcoming site trials will determine the next wave of AI adoption for the firm.
The company has not disclosed the specific costs associated with these deployments or the extent to which these tools are mandatory for subcontractors, which remains a meaningful unknown regarding the scalability of these systems.