Rifmont Group, a development and infrastructure firm with offices in Vancouver and New York, is building a private AI system trained on its own project data. The company says the system will pull together information from estimating, procurement, permitting, project sequencing, historical costs, and execution data to support internal decisions.
Few technical details have been released. The company has not disclosed the system's architecture, training methodology, or the volume of data involved. That silence appears deliberate.
"The objective isn't to build technology for the market. It is to build technology capable of competing with the market," said Jagroop Bhumber, founder of Rifmont Group.
The move comes as AI tools become widely available across construction. Bhumber's bet is that access to the technology itself will matter less over time than the proprietary information feeding it.
"Artificial intelligence itself will eventually become universal. The competitive advantage will come from systems trained on proprietary information unavailable to the broader market," Bhumber said.
Capital plans and AI strategy
Rifmont Group has previously outlined plans to deploy up to $10 million across real estate, development, and infrastructure. AI is expected to factor into that strategy, with capital potentially directed toward private computing infrastructure, proprietary datasets, and specialized technical talent.
Potential applications include procurement and permitting analysis, plus identifying cost anomalies and execution risks before significant capital is committed. For professionals working in AI for Real Estate & Construction, the distinction between building a system for internal use versus commercial release is central to how the company is positioning itself.
Data as the competitive edge
As powerful AI models become more accessible, the competitive advantage may shift from the models themselves to the data behind them. Proprietary project histories, specialized datasets, and accumulated operating intelligence are harder to replicate than a publicly available model.
That logic explains why Rifmont Group is keeping its system private. The company's project history and operating experience are assets competitors cannot license or download. Teams looking to build similar internal capabilities may find value in AI Data Analysis Courses to understand how proprietary data can be structured and used effectively.
Why this matters for real estate and construction professionals
For firms in development and construction, the takeaway is straightforward: the value of AI will increasingly depend on the quality of internal data, not the sophistication of the model. Companies that systematically capture project histories, cost data, and execution outcomes will be better positioned to use AI for competitive advantage. Those that treat data as a byproduct rather than an asset may find themselves at a disadvantage as AI adoption becomes standard practice.
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