M&A Tech Circle Paris

On 3 June, at la maison.ai, we held the first edition of the M&A Tech Circle with Arx and Aesus Advisory. On the agenda: an ecosystem announcement, a concrete demonstration of deliverable automation, and cocktails to keep the conversation going.

The idea behind the M&A Tech Circle is to bring together M&A professionals who take AI seriously, to strengthen the ecosystem and explore how technology is transforming the profession through real cases and first-hand accounts. This first edition delivered on that promise, with two highlights.

Entropia × Arx integration

Arx's CRM module, connected to the M&A ecosystem, lets you manage deal tracking from sourcing through closing. But visibility stops the moment due diligence opens in the data room. And that is exactly where the seriousness of acquirers shows: the depth of analysis, by firm and by individual. The technical integration between Arx and Entropia closes that gap.

The integration, announced by Julien Belon (Arx) and Pierre-Louis (Entropia), will let Arx clients:

• launch a data room, or a complimentary pre-data room, in one click from Arx, keeping their Arx identity and passing users and groups across, with no double entry;
• feed counterparty analytics tracked in the data room back into the CRM.

This integration is brought to you by the only two sovereign solutions, each in its own specialty (European companies, hosted on European cloud — Scaleway / OVH), not only to give you better visibility on your deals, but also to strengthen the strategic independence of European M&A.

Aesus Advisory: automating M&A deliverables, for real

Gabriel d'Agay, founding partner of Aesus Advisory, walked through a real, anonymised case: a buy-side deal, the consolidation-led build-up of an accounting firm. The stack comes down to two pieces: the Entropia data room (notably its built-in agentic workflows and its MCP server enabling collaboration with Claude), connected to a Claude project equipped with instructions, in-house skills (graphic charter, acquisition terms, DD templates drawn from past deals) and files.

Five deliverables were produced, from pre-LOI analysis to Due Diligence preparation.

Pre-LOI report

From the data room (call notes, three years of accounts, budget, legal org chart, payroll data): a complete report with history, org chart, revenue breakdown, an EBITDA bridge from reported to adjusted, points of attention, and a valuation range aligned with the firm's house guidelines. Plus a ready-to-use Q&A.

Financial model

P&L, balance sheet and cash flow over three years, an adjusted-EBITDA tab, a bridge, and a full LBO model: free cash flows, debt coverage, leverage.

Letter of intent (LOI)

A complete document: scope, valuation, governance, purchase undertaking, liquidity, financing, warranties, exclusivity, signatories, down to the personal wealth dimension. The first version was corrected by hand (formatting, a request for a proper bridge), a useful reminder that the human stays in the loop to arbitrate and validate.

IRL checklist

More advanced: an Entropia agentic workflow checks the data room's actual contents against the information requests sent, and flags what is missing. A check that used to be done by hand.

Due Diligence preparation report

Finally, a red flag report and Q&A preparation, again from the data room.

Across these demos, Gabriel walked through the setup in detail: how he assembled each of the skills used, the time it took to produce each deliverable, the importance of review and verification, and finally the impact on Aesus's productivity and its ability to raise the level of service it offers, especially to its small-cap clients.

Cocktail

The conversation carried on over cocktails with the banks and advisory firms present, all the richer for a room mixing MDs, AI taskforce leads and associates. Make or buy, productivity gains, sovereignty, opportunities for juniors: there was no shortage of topics.

• Auditability. Where it still breaks, and how to guarantee no error or omission.
• Productivity and quality. How far quality can rise at constant fees.
• Maintenance and replicability. The cost of maintaining the system and transposing it to a larger structure.
• Staffing. What becomes of junior roles and how they learn the trade.

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