Agentic AI startup FortyTwoMaru said on Thursday it will take part in the “Hwaseong City problem-solving urban data platform 구축” project, part of a 2026 public AI service support programme run by the Ministry of the Interior and Safety and the National Information Society Agency (NIA).

Hwaseong Special City, the lead agency, created a dedicated AI division in 2024 and is now providing data-driven smart administration centred on its AI Smart Strategy Office. The platform will be built to solve urban problems that public officials and citizens can feel in practice.

The project aims to implement the Korea Research Institute for Human Settlements' prior research on analysing and predicting civil-complaint big data in an actual local government administrative system.

Hwaseong will move beyond the limits of administration at the eup, myeon and dong level by predicting in advance the potential risk of civil complaints at the level of national basic districts. It will first link 38 databases among the city's 63 information systems and weave dispersed municipal data into an urban ontology (knowledge graph) model to build a foundation for intelligent city operations that detects and responds to anomalies pre-emptively.

FortyTwoMaru will form a consortium with lead contractor SOCsoft and ICTWAY to take charge of the AI component. It plans to support advanced language AI technologies, including development of agentic AI for analysing and predicting civil complaints linked to the pan-government AI common-based infrastructure, AI-based automatic recommendations of responsible departments based on complaint content, structuring unstructured administrative documents and intelligent RAG (retrieval-augmented generation) search.

Kim Dong-hwan (김동환), CEO of FortyTwoMaru, said the project would realise administrative innovation that citizens can directly feel by combining the pan-government AI common-based platform with on-the-ground local government data. He said the company would help shift civil-complaint administration from after-the-fact handling to proactive predictive administration and contribute to building a successful standard model for local government AX.