AI Due Diligence Auditor

Document-Gap Analysis + Findings Triage

Demo mode: the property, data room and findings below are entirely fictional. The pipeline runs live AI calls to audit the documentation and triage the inspection findings against a surveyor's own classification.

Maple Court Commercial Building

Mixed-use office & ground-floor retail · built 1985 · 8,420 m² · 4 floors

A four-storey 1985 mixed-use building with ground-floor retail and three floors of office space. Last major refurbishment in 2006. Under offer; the buyer commissioned a technical due-diligence review of the data room and a site inspection.

AI Due Diligence Auditor

Case Study

Industry

Real Estate / Commercial Property Transactions

Context

Built for an advisory team that produces technical due-diligence reports when commercial buildings change hands. Every deal means reviewing a data room for completeness and triaging dozens of inspection findings into a consistent, defensible report — under time pressure. This demo uses an entirely fictional property and data set.

The Challenge

Due diligence is slow, manual and easy to do inconsistently. Two reviewers can classify the same finding differently, and a missing document only surfaces late when it blocks completion. Buyers need a clear, plain-English view of what's wrong, what it costs, and what paperwork is still outstanding.

Pain Points

✖Checking a data room against a required-document checklist is tedious and error-prone

✖Findings get classified inconsistently between reviewers and across reports

✖Cost estimates and priorities are scattered, not summarised for a decision-maker

✖Turning raw findings into a buyer-ready report takes hours of write-up