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MEP equipment accounts for up to 40% of costs on data center or hospital projects, has lead times ranging from 20 weeks to over a year, and has historically been the most underserved area in construction software. In this episode<\/a>, I speak with Victor Muchiri<\/strong> from BuildVision<\/strong> about what it actually takes to make AI useful in construction procurement, not as a pilot, but in production.<\/p>\n We dig into why you cannot simply upload a set of construction drawings to ChatGPT and trust the output. Construction documents are complex, cross-referenced, and consequential. Without deep domain context, such as manufacturer ontologies, equipment taxonomies, and engineering expertise, AI produces plausible results, not reliable ones. BuildVision\u2019s approach is to act as a harness around AI models, wrapping them in construction-specific knowledge so the output can be trusted for real procurement decisions.<\/p>\n We also discuss what structured equipment data enables for general contractors: earlier decision-making, lower procurement risk, and visibility across projects. And we touch on BuildVision\u2019s outcome-based pricing model, a signal that AI in AEC is maturing toward measurable, accountable value delivery.<\/p>\n Victor Muchiri<\/a> is a business development leader at BuildVision, an AI-powered procurement platform for the construction industry. Find BuildVision at buildvision.io<\/a> and on LinkedIn<\/a>.<\/p>\n Watch the episode on YouTube.<\/a><\/p>\n Podcast: Play in new window<\/a> | Download<\/a><\/p>\n Subscribe: <\/p>\n <\/div>\nListen here or on your podcast platform of choice<\/h2>\n