AI & technology.

Manual CRE underwriting takes 4 to 8 hours per deal. Here is where the time goes, task by task, and how much of it AI removes.

David Fields joined Michael Pouliot on the DealFlow Podcast to talk through how AI reshapes CRE underwriting and pipeline management, and why it doesn't kill the junior analyst role, it expands it.

The internal build looks cheap until you scope the CRE domain accuracy problem, SOC 2, and the 6–12 month timeline. Here's the real math on build vs. buy.

The legacy CRE stack is being challenged by AI-native platforms. Here's what's changed, who the new entrants are, and what it means for acquisitions teams.

Manually rekeying T-12 data from PDFs is the single biggest time sink in CRE underwriting. Here is how AI-powered extraction tools work, what accuracy to expect, and how to evaluate them for your deal pipeline.

Offering memorandums are 40-page PDFs designed to sell, not to inform underwriting decisions. Here is how to evaluate software that reads OMs automatically, extracts deal economics, and flags discrepancies before you spend hours building a model.

Using Claude as a one-off chatbot is just the tip of the iceberg. The firms building for the future are weaving AI into their organization's DNA, giving leadership real-time pipeline visibility and flagging deal-killing risks before $50k is spent in legal fees.

Companies investing heavily in AI are growing revenue 5x faster, and the gap is compounding. In CRE, the parallel is stark: while your team spends 5–10 hours modeling each OM, AI-powered firms are screening 20+ deals a month and killing bad deals before burning $50k in diligence.

How AI-powered tools are reducing underwriting time from days to hours while improving accuracy and consistency across deal teams. A full breakdown of capabilities, ROI analysis, and platform evaluation criteria.