What diligence looks like when you can see the logic.
Diligenz AI is built to be used by different types of deal teams at different points in a transaction. The workflow varies. The underlying problem does not.
Every team is working from imperfect information. Every team is trying to understand a business in a compressed timeframe. And every team is taking on risk they cannot fully quantify with the tools they have.
Buy-Side Due Diligence at a PE Firm
A mid-market PE firm is in exclusivity on a $180M acquisition. The target has provided a three-statement model spanning 24 tabs. The deal team has 10 working days before the LOI needs to be finalised.
The analyst uploads the model to Model Maestro. Within the first pass, three issues surface: a revenue line that is hardcoded rather than formula-driven, a working capital assumption that does not reconcile between the balance sheet and cash flow tabs, and a tax rate that appears to have been fixed at a rate inconsistent with the company's jurisdiction.
None of these would have been visible from reading the model. They required tracing the logic.
The team uses the findings to structure a targeted Q&A with the seller, rather than spending three days building their own parallel model to verify what should already be there.
Legal-Financial Reconciliation on a Cross-Border Transaction
A corporate legal team is running diligence on a cross-border technology acquisition. The data room contains 340 documents. The financial model was built by a third-party adviser. No one has checked whether the two are consistent.
Contract Inspector processes the data room. Diligenz AI extracts clause-level detail from the share purchase agreement, IP assignment documents, and employment contracts. These are cross-referenced against the financial model automatically.
Four discrepancies are flagged. The most significant: the IP assignment excludes technology developed before a certain date, but the target's revenue model attributes full value to pre-exclusion IP. The financial model and the legal agreement are telling two different stories about what is actually being acquired.
The legal team catches this before signing. Not in a post-close review.
Growth Assumption Validation on a Sell-Side Process
An investment bank is running a sell-side process for a founder-owned business. The management team has prepared projections showing 22% revenue growth over the next three years. The bank needs to know whether these projections are defensible before they go into the CIM.
Deal Scout benchmarks the revenue growth assumption against comparable businesses in the sector using the Syfter integration. The sector median for the past three years is 9%. No comparable in the relevant size band has sustained more than 14% over a 36-month window.
The management team's projections are not impossible. But they are at a level that requires a specific, identifiable driver to be credible to a sophisticated buyer.
The bank uses the benchmarking output to pressure-test the assumptions with management before the CIM goes out, rather than having a buyer use the same data to discount the asking price.
Talk to the Team About Your Deal Type
Book a 20-minute call. Bring the use case. We will show you what Diligenz AI finds.