We built a 100-document acquisition data room — roughly 10,000 pages — and put it to every option a buyer really has: a consumer AI, a due-diligence firm, the new "AI advisory board" tools, and 3Dogs Nexus. Then we ran two tests: would it bluff on a thin room, and could it find risks deliberately buried in a rich one?
A 10,000-page data room is ~1.15 million tokens across 100 separate files. That single fact decides most of the contest before a word of analysis is written.
The tool most people reach for. It hits hard product limits before analysis begins:
The traditional answer — analysts read the room by hand: quality-of-earnings, working capital, legal, tax.
Its Deep Discovery engine map-reduces all 100 documents into one grounded, cross-referenced brief, then runs an adversarial panel over it.
Our first run used a deliberately empty 10,000-page room — volume with no real financials behind it. A single confident AI will happily manufacture an authoritative acquisition memo from thin air. 3Dogs read every page, detected the room was substantively empty, capped its confidence at Low (73%), and printed the dissent — three of eleven analysts arguing against proceeding on an incomplete room:
That's the point. The value wasn't a confident yes — it was an honest “not yet, and here's exactly what to demand first.” (601 calls · 13 models · 12m 03s.)
Our second run used a rich data room with eight specific, deal-changing risks hidden inside — each in one place, surrounded by thousands of pages of ordinary paperwork. Because we built it, the test is falsifiable: here's the answer key, and how 3Dogs surfaced each one.
| The risk we planted | Where we buried it | How 3Dogs surfaced it |
|---|---|---|
| Customer concentration | Customer revenue schedule + a sales-team email | ✓ CAUGHT Flagged Nevada Copper & Mining = 41% of revenue, contract up for renewal — made escrow on concentration a condition. |
| Undisclosed environmental liability | A Phase II environmental report, 1 of 100 files | ✓ CAUGHT Surfaced the NDEP remediation exposure ($1.4–2.2M) not reserved in the financials. |
| An earn-out built to fail | The LOI vs. the historical financials | ✓ CAUGHT Did the math: “$6.2M EBITDA by FY2027 requires 41% growth from a declining revenue base — probability-weighted achievement is low.” |
| Inventory overstatement | An inventory aging report + the balance sheet | ✓ CAUGHT Flagged $2.6M of obsolete / >360-day inventory with no reserve. |
| Inflated EBITDA add-backs | A quality-of-earnings add-back schedule | ✓ CAUGHT A dissenting analyst named the “systemic lack of supporting documentation for EBITDA adjustments.” |
| Key-person risk | An HR file + one line in an email | ✓ CAUGHT Surfaced D. Marsh — senior account manager, no non-compete, signaling retirement. |
| Undisclosed pending litigation | A single footnote in a legal memo | ✓ CAUGHT Pulled the $900K product-liability suit into “open liabilities” requiring escrow. |
| A revenue contradiction across documents | The CIM ($46.0M) vs. the tax return ($42.3M) | ✓ CAUGHT Caught the $3.7M topline overstatement between the marketing memo and the filed return. |
A wave of multi-agent "decision" products has launched. They're genuinely useful — for the jobs they're built for. But this use case, an unstructured 10,000-page document room, exposes what each is actually architected to do.
| Tool | What it is | On a 100-file / 10,000-page room | 3Dogs difference |
|---|---|---|---|
| DECISO | A 7-persona "decision council" (financial strategist, risk analyst, ethicist…) | Not built to ingest a document room — advises from personas, not bulk evidence | Reads all 100 docs, then debates the evidence |
| SynthBoard | Up to 24 persona "synths," ≤8 per session; credit-metered per turn | Accepts a few PDFs, but a 100-file room blows the credit model; personas run on static knowledge | Distinct models, flat unlimited, map-reduce over the whole room |
| Edge Arena | A "decision trial" — agents argue competing strategies | A strategy trial, not a document reader; free runs are public — no place for a confidential data room | Private by default; grounded in the actual documents |
| Dot (GetDot.ai) | An AI analyst for your structured data warehouse (Snowflake, BigQuery…) | Needs SQL tables, not a pile of PDFs — a 100-doc room isn't its input at all | Purpose-built for unstructured document rooms |
| 3Dogs Nexus | Deep Discovery + an adversarial panel of distinct models | Ingested all 100 docs / 10k pages; 8/8 buried risks; decisive RENEGOTIATE in 28 min | — |
Competitor capabilities and pricing reflect each vendor's published materials (July 2026) and are summarized for this specific document-heavy use case — not a knock on tools built for other jobs. The distinction here is architectural: persona boards and warehouse analysts aren't designed to read an unstructured 10,000-page due-diligence room.
Both deliberated briefs — the empty-room integrity run and the 8/8 buried-risk run. Every claim, the confidence, the vote, the preserved dissent.
Case 2026-0084 · RENEGOTIATE · 1,302 calls · 12 models · 28m 04s.
Open the flagship report (PDF)Case 2026-0083 · refused to bluff, Low confidence · 601 calls · 13 models · 12m 03s.
Open the integrity report (PDF)