From Classic ML to Agentic Predictions

How DealSumm evolved from one-shot model outputs to an orchestrated system that retrieves evidence, reasons with structure, and verifies every claim — without burning compute. If you have ever built ML for contract analytics, you know the pattern: you ship a model that looks great on a benchmark, then reality shows up — one client has a different template, another uses a jurisdiction-specific term, and a third buries the key clause in an amendment scanned in 2008. At DealSumm, our goal is not just prediction — it is turning messy commercial lease documents into trustworthy, referenced data that teams can review, analyze, and act on inside a single AI-ready platform. The problem is that lease abstraction is not a single prediction problem — it is a chain of dependent mini-decisions: find the right clause, interpret it in context, map it into a client schema, and then prove you did not hallucinate.