You're right to be suspicious.
If you work deals for a living, you have seen what AI usually means: a confident summary, no sources, wrong in the one place it mattered. That reputation is earned, and this page does not ask you to forget it.
It asks a narrower question instead: on the mechanics of due diligence, where does a structured research machine beat a human analyst, and where does it lose? Then it hands you a way to check.
Four things it does better than any human analyst.
Not better at everything. Better at these four, which happen to be where most diligence quietly fails.
It recomputes every figure from the raw cells instead of trusting the displayed totals, and it reads page four hundred at the same depth as page one.
In a recent test file it flagged direct labour growing at exactly 5.00%, to the basis point of revenue growth. That is the discrepancy a tired human skims past.
Exclusivity windows and LOI clocks do not wait for a six-week diligence calendar. A full Verdict lands in five days, a Micro-DD in 48 hours, and the rubric never queues behind another client's deal. The machine works at the speed the deal actually gives you.
Traditional diligence starts at five figures, so below a certain deal size it simply never happens. At $750 flat, the honest comparison for most SMB deals is not machine versus a top human team. It is machine versus no diligence at all.
Almost everyone at the table gets paid when the deal closes. The broker, the lender, the advisor billing hours toward a completion.
The machine has no success fee and no relationship to protect, and one pass is explicitly paid to argue the deal fails. Its only job is the call being right.
The machine researches. A person signs.
Machines beat humans at recall: finding, reading, cross-checking. Humans beat machines at judgment. Verdict splits the work exactly on that line.
Recall, coverage, and the discipline of sourcing
The part of diligence that is really industrial reading, done industrially.
- Ten dimensions researched in parallel, one agent each
- Every material claim cited to a source you can open, or flagged as inference
- An adversarial pass hunting for the case against the deal
- A machine-checked claim table and consistency linter before anything ships
Judgment, context, and a name on the line
The part a model should not be trusted with, so here it is not.
- Key facts checked by hand before delivery
- The verdict weighed and signed by one person, Fabi, on every report
- Thin evidence said plainly, never dressed up as a finding
- A refund if a publicly knowable red flag was missed, in writing
We tested it on a deal that went bankrupt.
Before selling reports, we ran the engine blind on a $2.1M acquisition a buyer had already closed. It surfaced every issue public records could reach and said do not close. The business later went bankrupt.
Then an evaluator built a synthetic company file specifically to fool it. Every planted issue surfaced.
Every issue that public records could reach on the bankrupt deal, the blind lanes reached. Nothing an outsider could have seen was missed.
Formulas in a purpose-built trap file, recomputed from the workbook's own cells rather than the displayed totals. Every total tied, every planted issue caught.
The acquisition the engine was run on blind. The report said do not close. The buyer had closed, and the business went bankrupt.
The full finding-by-finding scoring, gaps disclosed, including three published investigations graded the same way: the backtest · how we tested
Don't take the argument. Take the test.
Send a deal you have already closed and diligenced. The engine runs it blind, and you grade the report against what your own team found. No card, no call, and you keep the report either way.
Written and async, start to finish · every claim sourced or labelled
Backed by the Verdict guarantee: useful, or your money back.