Pune has opened an AI-based building diagnosis experience centre that will demonstrate how emerging technology can identify structural and maintenance-related problems before they worsen. The centre has been jointly established by Gruh Sanjeevan Foundation and Foxscan to support more informed, practical and affordable building inspections.
The facility will showcase the use of artificial intelligence in detecting building problems at an early stage and helping owners and maintenance teams make better repair decisions. The system has been developed using data collected from inspections of more than 40,000 buildings, according to Abhishek Jagdale, founder and chief executive officer of Foxscan.
The technology is intended to assess issues including moisture, dampness and surface deterioration. Early identification of these problems could allow them to be evaluated before repair work, painting or interior renovation begins. The developers said this may help reduce unnecessary repairs, repeated interventions and wastage of construction materials.
The building diagnosis system combines high-quality imaging with an integrated laser system for measuring surfaces. It also uses artificial intelligence and machine learning for defect analysis, along with structural-strength assessment capabilities. The system is designed to produce automated diagnostic reports and suggest possible solutions and repair measures.
Jagdale said conventional thermal imaging systems primarily identify differences in temperature. Foxscan’s system, by contrast, has been developed to bring inspection, analysis and diagnosis into a broader structured process. The stated objective is to make building diagnosis more comprehensive and useful for maintenance planning rather than limiting assessment to a single imaging output.
The experience centre is positioned as a demonstration and learning facility for the use of AI in building maintenance in India. Its development reflects an attempt to adapt diagnostic tools to local construction and building-maintenance conditions, although the source report does not specify the centre’s operating capacity, inspection charges, locations covered or the regulatory status of its automated recommendations.
For building owners and housing societies, the system’s proposed role is preventive: identify deterioration, understand its likely extent and plan intervention before defects become more extensive. The technology is therefore presented as a support tool for inspection and maintenance decisions. The report does not state that AI-based assessment replaces qualified structural engineers or other statutory inspection requirements.
The centre’s next phase will involve demonstrating how the system can be used to identify building problems, generate diagnostic reports and inform possible repair actions. Further details on its public access, deployment model and adoption by housing societies or other building owners have not been announced in the supplied report.