The data room built for teams who work with AI

M&A teams increasingly work from AI platforms

M&A professionals are no longer using AI to tidy up emails. They use AI to do the work: build the EBITDA bridge, draft the LOI, prepare the red flag report, assemble the Q&A. The Economist ("Will anybody use AI as much as coders do?", 30 August 2026) names finance as one of the three candidates to follow software engineering as a runaway application, and notes that Rogo added a hundred enterprise customers in a quarter while growing recurring revenue by 50%. (read our partnership announcement)

We see the same thing inside deals. At a recent M&A Tech Circle in Paris, Gabriel d'Agay of Aesus Advisory demoed five deliverables from a live buy-side build-up, from pre-LOI analysis to due diligence preparation, all produced from an AI platform connected to our data room. Our own number: on 40% of Entropia data rooms, admins have connected their AI platform over MCP, the Model Context Protocol, the open standard that lets an AI platform call another software's tools directly.

This changes the test for every vendor in the M&A stack. Software is now judged on how it behaves inside a chat window. Deal teams ask whether a tool connects to their platform before they ask what its interface looks like. And the real question becomes how well it connects.

The deal information should be accessible

The pre-LOI report from the Aesus demonstration drew on three years of accounts, a budget, a legal org chart and payroll data, all in the data room. It also drew on call notes, the firm's valuation guidelines, its due diligence templates and its graphic charter. None of that belongs in a data room. Widen the frame and the list grows: sector reports, comparables, VDD reports, management meeting transcripts.

Two consequences.

A data room's own AI cannot produce M&A deliverables alone, however good it is, because it only sees the data room. We build agentic workflows inside Entropia and we are confident in them. They still only know what is in the data room.

A bank's AI platform cannot produce it either if the data room gives it a narrow pipe. The platform can hold the firm's precedents, skills, templates and transcripts and still fail on a change of control clause analysis because it cannot search the data room properly, or cannot read past the first page of a contract, or cannot see the Q&A, etc.

That is the state of the VDR market. Most data rooms expose no MCP server at all. Among the few that do, including the largest names, the tools are thinner than what the same vendor keeps for the AI layer it sells separately: partial reads, weak or absent search, no write, no source display, no Q&A, no audit. Sometimes that is because their teams are technically limited. But more structurally, it is often a commercial decision: a vendor charging a licence fee for its own AI has no reason to let a competing AI do the job properly.

Deal teams tell us in private that the in-house AI those licences pay for is not the one they want. They have chosen their platform, built skills in it and put the firm's knowledge next to it. Being asked to leave it, and to pay for the privilege, is a poor trade.

The best of both worlds

Entropia has another strategy. A "focused on the user" strategy. The tools our own AI uses inside the data room are the tools your AI platform gets, at the same depth, with nothing held back for a paid tier.

The details matter for your teams to be able to work properly:

• Reading. Page-range reads across PDFs, Word documents, slide decks and images, so an agent works through a two-hundred-page agreement in sections. Spreadsheets read sheet by sheet as tables. A document opens at a given page inside the conversation, with the cited passage highlighted, so the reviewer checks the source rather than a transcription of it.
  Every read comes back marked readable, unreadable or redacted, and only the first may be reported as what the document says. An agent that cannot read a file is told so in terms it cannot paper over. That is the difference between a tool a deal team can rely on and one that writes confident prose about a page it never saw.
• Searching. Full-text search across the entire data room with fuzzy matching, returning path, page number and excerpt. A reference like 1.2.3 resolves to the actual file, so analyst and agent talk about the same document in the same terms.
• Q&A, in full. The Q&A is where a deal team spends its evenings, and it is usually the first thing a connector drops. Ours exposes the whole workflow: a search index over threads (stream, author, question number, message content), each conversation in order with roles and authors, the counterparty group behind each stream, and the ability to compose a proposed answer.
  On that last tool, precision matters. The proposed wording lands as an editable draft in the reply composer of the answering side. The asking side never sees it. The tool cannot post or publish. A human reads, edits and sends in the application, and no configuration skips that step.
• Writing, narrowly and reversibly. Reorganise the index, tag documents, compose a group's access before inviting it. Sending to the bin is reversible. Write access is opt-in per connection, and your AI client can allow, block or require confirmation on each tool separately.
• Access and activity. Read the effective permission matrix over a folder subtree, including descendants whose access differs from their parent. Diff two counterparty groups to see what each can reach that the other cannot, with exact counts. Aggregate over the audit log by date range, event family, group, role, user or free text, to see who read what and what nobody opened.

This works from both sides of the deal. A sell-side adviser runs the data room from their platform. If allowed, a bidder analyses the data room from its own platform, inside its own permissions, seeing exactly what it was given access to. The server only lists the data rooms where the user's group has been granted AI access, which each data room's administrators control.

Every MCP tool call executes with the rights of the person who authorised the connection, never beyond them, and is recorded in the audit log under their name like any other action. A seller buys a data room to control and prove disclosure. An AI connection that weakened that guarantee would defeat the product. Ours strengthens the record: an agent's every read lands in the same log as a human's.

Entropia has a native connector in Claude, Rogo, Model ML and others. Connecting your AI platform to your Entropia data room costs nothing on top of your subscription.

Four questions for your current provider

1. Does it expose an MCP server at all?
2. If it does, can the connection read a whole document, search the entire data room, work the Q&A, and show the audit trail? Read tools only, or write and display too?
3. Is the deepest tooling available to your AI platform, or reserved for the AI layer they want to sell you?
4. What does the connection cost?

The answers sort providers into two groups. Only one is helping you work where you already work.

Book a demo and we will connect your AI platform to a live Entropia data room, with your team in control.