This case study explores how startup AIConstruct is developing an AI-integrated immersive platform designed to help construction teams compare planned building models with on-site reality. Founded by software developer and Cesium-certified engineer Nompumelelo (Lelo) Mtsweni, the company aims to reduce cost overruns, delays, waste and corrective work in the construction sector. Participation in The Turing Way Practitioners Hub has influenced the project’s development – particularly in areas such as data readiness, international standards, and version control. This case study is published under The Turing Way Practitioners Hub 2025-26 Cohort - case study series. The Practitioners Hub is The Turing Way project that works with experts from partnering organisations to promote data science best practices. Key takeaways Comparing planned building models with real-time site data can help reduce delays, cost overruns and unnecessary waste or corrective work in construction projects – all chronic problems in the sector. Integrating multiple technologies – rather than relying on a single breakthrough innovation – creates technical challenges but can produce practical product benefits and efficiencies. Similarly, open-source tools can enable cost-effective innovation but require careful integration and maintenance. Data readiness, version control and machine-readable formats are among the factors crucial to building responsible AI tools and generating reliable outputs. Rule-based AI can assist compliance checking by incorporating the 1,000+ global standards for the built environment sector. Trust-building, patience, humility and clear communication are important when introducing novel technologies into traditional, relationship-based industries such as construction.
Mtsweni et al. (Thu,) studied this question.
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