AI due diligence software
How Vaultrix does AI due diligence
The product mechanics: how a data room becomes cited answers, tracked requests, and flagged gaps and conflicts. New to the concept itself first? Read what AI due diligence is before diving into the implementation.
01What is AI due diligence, and how does Vaultrix do it?
AI due diligence is the use of AI to read a transaction data room, answer diligence questions with citations back to the exact source, and flag what evidence is missing or conflicting — instead of a deal team manually reading every file to find the same information.
It isn't a chatbot bolted onto a data room. The analysis is grounded in the documents actually uploaded for that deal, every claim traces to a page, slide, or spreadsheet cell, and a person reviews the findings before anything counts as final.
02How does AI due diligence work?
In Vaultrix, it's one continuous workflow: upload the room → map the request list → find evidence → identify gaps and conflicts → verify citations → review and sign off.
The data room is uploaded as a single ZIP and indexed. Diligence questions are answered against that index with citations. Your own DD request list is mapped against the same evidence. High-risk answers get a second, independent citation check. Everything lands with your team for review — see it happen in the interactive demo.
03What does an AI diligence analyst do?
It reads the whole data room and reasons across documents to answer the questions an associate would ask, works through your request list, and surfaces the evidence, gaps, and conflicts it finds — it doesn't produce an unverified summary and call it done.
04What documents are analyzed?
PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), CSV, and plain text — uploaded once as a single ZIP archive of up to 500 files. One honest limitation: there's no OCR yet, so a scanned, image-only PDF won't have extractable text. Native/digital PDFs work fine.
05How are answers grounded in evidence?
Every answer is required to cite the page, slide, or spreadsheet cell it came from. If the model can't point to a source, it says so instead of guessing. That doesn't make it infallible — it means every claim is checkable against the original document.
06How are citations generated?
Retrieval is scoped to the specific deal, the answer is generated from the evidence chunks that were actually retrieved, and the citation links back to the precise location — the page, the slide, or the sheet and cell range — in the source file. On high-risk questions, a second, independent model re-checks every citation against the source text and flags anything it can't support, instead of presenting it as fact.
07How are missing evidence and conflicts identified?
Every item on your request list gets one of four outcomes: evidence found, partially supported, no evidence found, or conflicting — the last one when two source documents disagree on the same fact. Gaps surface during diligence instead of after closing.
08How does a diligence request list work?
Upload the DD request list you already use, from Word or Excel. Vaultrix maps each line to the evidence in the data room and proposes one of the four outcomes above. It never closes a request on its own — every status stays subject to human review.
09Why is human review still important?
The AI performs the first pass. Your deal team reviews every finding, missing item, and conflict, and signs off before anything counts as closed. That's not a formality — it's what catches the cases a model gets wrong, or a source document leaves genuinely ambiguous.
10How is this different from generic AI chat?
A general-purpose chatbot is usually capped at a few files per conversation and summarizes without a verifiable source trail. Vaultrix ingests an entire data room as one upload, reasons across every document in it, and requires every answer to cite the exact page, slide, or cell it came from.
11How is this different from a traditional data room?
A data room stores and permissions your documents; it doesn't answer questions or map evidence to a request list for you. Vaultrix complements your existing diligence stack — it's built on top of that same need, not asking you to replace how you store and control access to files. See the full AI data room explainer or the comparison section.
12What are the limitations of AI in due diligence?
AI due diligence accelerates the first pass through a data room; it does not replace professional judgment. A model can miss context a human reviewer would catch, can only find evidence in documents that were actually uploaded, and cannot certify that a data room is complete.
That's the reason the workflow ends with human review rather than an AI-generated sign-off: findings, gaps, and conflicts are proposals for a deal team to confirm, not conclusions to act on unverified. Vaultrix does not claim perfect accuracy, and it will say when it cannot find supporting evidence rather than guess.