Diald.AI, a technology company recently founded by Steven Song, has developed an artificial intelligence platform aimed at addressing a longstanding challenge in commercial real estate: the persistent difficulty of obtaining a truly comprehensive, context-rich understanding of a property, particularly when the site is in an unfamiliar neighborhood or city.
Traditional due diligence methods often leave investors, lenders, and developers with only a partial view, relying on a mix of limited public data, anecdotal insights, and time-consuming manual research. As Song puts it, “you cannot know everything about a place, and especially if it's new, you know, it's a new neighborhood that you're not already familiar with.”
This gap is especially acute because real estate decisions hinge not only on hard numbers but also on a deep grasp of local dynamics, risks, and opportunities—factors that are often qualitative, nuanced, and hidden from standard datasets. Even large firms, Song notes, resort to “a little bit of Google search,” phone calls to contacts, and site visits, all of which still leave them “operating with limited, partial data,” and vulnerable to human bias.
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Song's system leverages agentic AI to process data from approximately 1.7 million sources within a few hours, producing a detailed investment memo for users.
According to Song, the platform identifies the necessary datasets to fully understand a location, utilizing 19 specialized AI agents that emulate the expertise of human consultants in fields such as zoning, infrastructure, and policy shifts.
The investment memo generated by Diald.AI includes an executive summary, market study concepts, rent comparisons, SWOT analysis, and a proprietary “Diald Score.” This score provides a probability of success for a project, benchmarked against historical outcomes in similar contexts.
The report, which can be hundreds of pages long, ranks thousands of findings and highlights the most significant insights for investors.
It works like this: Clients log into a dashboard and input basic information such as the property address, their role (investor, lender, operator, etc.), and details about the current and future use of the site. The AI then processes this information and delivers a comprehensive report via email and the dashboard within a few hours.
Since its market launch in January, Diald.AI has secured four large contracts. Song stated that, contrary to his initial expectations, the first adopters were some of the largest companies in the industry, likely due to their volume of transactions and desire to maintain a competitive edge.
From a technological perspective, Song describes Diald.AI as the first proptech application to conduct qualitative analysis at scale, which he believes is a critical factor in effective real estate investment. He claims the tool is about ten times faster, ten times cheaper, and approximately one hundred times more thorough than traditional, human-driven analysis.
Diald.AI plans to expand its geographic reach beyond the United States and Canada, with patents already secured in Japan, Korea, and the European Union. The company also intends to introduce new functionalities, such as client-specific performance projections.
Song expressed the hope that Diald.AI will be a reliable source of truth for real estate investment decisions, allowing stakeholders to build, fund, and lend to the right projects with greater confidence. He suggested that the platform could help mitigate periodic crises in the real estate sector caused by incomplete analysis and human error, stating, “Hopefully this avoids that, and allows us to use land a little better.”
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Erika Morphy
Erika Morphy has been writing about commercial real estate at GlobeSt.com for more than ten years, covering the capital markets, the Mid-Atlantic region and national topics. She's a nerd so favorite examples of the former include accounting standards, Basel III and what Congress is brewing.
