[{"data":1,"prerenderedAt":569},["ShallowReactive",2],{"blog-de-internal-ai-workflows":3,"blog-related-de-internal-ai-workflows":138},{"id":4,"title":5,"author":6,"body":7,"coAuthors":122,"date":124,"description":125,"detail":126,"extension":127,"featured":126,"image":128,"imageSize":129,"meta":130,"navigation":131,"path":132,"railTitle":129,"seo":133,"stem":134,"summary":129,"tags":135,"unlisted":126,"updatedAt":129,"video":129,"__hash__":137},"blog\u002Fblog\u002Fen\u002Finternal-ai-workflows.md","Our internal AI workflows","Pierre-Louis",{"type":8,"value":9,"toc":114},"minimark",[10,14,17,20,25,28,31,34,41,45,48,51,54,60,64,67,70,73,76,80,83,92,95,103,106],[11,12,13],"p",{},"Enterprise AI is usually sold as a new colleague: the autonomous employee that reads your codebase, files the tickets, ships the fix and updates the documentation while the founders sleep. It is a tidy story, and almost nobody runs their company on it. The agents that have earned a place in a working engineering team are far less cinematic. They take over a specific, repetitive chore, they do it quickly, and they hand the result back to a person who decides what happens next.",[11,15,16],{},"Last week Anthropic held an event at our Paris office, and our engineering team put three of these on the table. None of them runs the company. Each one removes a particular kind of toil that used to eat an engineer's afternoon, and in every case a human still signs off before anything reaches a customer. That second part is not a caveat we bolt on for comfort. It is the design. An agent that scales the work while a person keeps the judgment is a throughput multiplier you can actually trust. An agent that decides on its own is a liability waiting for its first bad day.",[11,18,19],{},"Here is what that looks like in practice.",[21,22,24],"h2",{"id":23},"the-sre-agent-compressing-the-slow-part-of-an-incident","The SRE agent: compressing the slow part of an incident",[11,26,27],{},"Site reliability work has a familiar shape. Monitoring flags an anomaly, and then the clock starts on the part that hurts: figuring out what broke. The fix is often quick once you know where to look. The looking is what costs you, usually at an inconvenient hour, usually under pressure, usually across a week's worth of changes any of which could be the culprit.",[11,29,30],{},"Our SRE agent goes after exactly that bottleneck. When monitoring flags an anomaly, the agent pulls the latest code changes, checks them against the symptom, and proposes a likely cause within minutes. It does not deploy anything. It does not roll anything back. It narrows the search and tells the on-call engineer where to start, and the engineer decides and fixes.",[11,32,33],{},"The value is not that the agent is clever. It is that diagnosis, the genuinely tedious detective work, no longer depends on one person.",[11,35,36],{},[37,38],"img",{"alt":39,"src":40},"Presenting the SRE agent","\u002Fblog\u002Fdogfood-sre-agent.jpg",[21,42,44],{"id":43},"the-help-center-agent-documentation-that-keeps-pace","The help-center agent: documentation that keeps pace",[11,46,47],{},"We ship every week. Documentation rots accordingly. Help articles are right on the day they are written, but the product moves and the articles do not, so a customer reads last quarter's screenshot of a screen that no longer exists.",[11,49,50],{},"Our help-center agent reads each new release, detects the features that are new or changed, and drafts or revises the matching article. When a screen has moved, it takes fresh screenshots of the relevant parts of the app so the visuals match what the customer is actually looking at. Then the product team reviews the changes before anything is published.",[11,52,53],{},"That review step is the whole point. The agent does the labour of keeping a sprawling help center current, which no human enjoys and few do consistently. A person still owns the words a customer reads. The result is documentation that tracks the product week by week instead of trailing it by a release or three, without a quarterly scramble to catch up.",[11,55,56],{},[37,57],{"alt":58,"src":59},"Presenting the help-center agent","\u002Fblog\u002Fdogfood-helpcenter-agent.jpg",[21,61,63],{"id":62},"the-worktree-switcher-space-for-several-agents-at-once","The worktree switcher: space for several agents at once",[11,65,66],{},"The third example is not an agent at all, and that is exactly why it matters.",[11,68,69],{},"A worktree switcher lets a developer keep several versions of a project open on the same machine at the same time, rather than being pinned to one. On its own that sounds like a minor convenience. It becomes important the moment you start using AI coding agents in earnest. You do not want to run one and wait; you want to run several in parallel, each in its own isolated workspace so they cannot overwrite each other's work, and switch between them without friction.",[11,71,72],{},"This is the unglamorous plumbing that makes parallel AI coding practical instead of chaotic. The constraint is no longer how many agents you can point at a problem, but how many a developer can supervise at once. The supervising, again, stays human.",[11,74,75],{},"A small theme runs through all three. The agent absorbs the part of the job that scales badly with human attention. The human keeps the part that should not be automated at all.",[21,77,79],{"id":78},"the-same-goes-for-the-agents-we-deploy-for-our-customers-in-the-data-room","The same goes for the agents we deploy for our customers in the data room",[11,81,82],{},"This is the principle we built our product on, and shipped to customers first.",[11,84,85,86,91],{},"Entropia was the first data room to launch an MCP server, in 2025, letting customers connect their own AI platform directly to their data room. Most of them use it with Claude. (More on ",[87,88,90],"a",{"href":89},"\u002Fblog\u002Fmcp-server-launch","the launch of our MCP server",".)",[11,93,94],{},"For the uninitiated, MCP, the Model Context Protocol, is an open standard that lets an AI assistant connect to an external system and act inside it through a defined set of permitted operations. The power of this MCP connection then depends on many factors, such as the sophistication of the AI platform connecting to it, but also the tools the data room makes available to it.",[11,96,97,98,102],{},"We draw the same line for customers: the AI can read, search, answer and scale across thousands of documents, and a person still validates the outcome and owns the trail of what was done. Our ",[87,99,101],{"href":100},"\u002Fblog\u002Fautonomous-irl-tracker","autonomous IRL tracker"," is a good example, checking every line of a request list against the data room and returning the file marked complete, partial or missing, ready for review. The discipline is identical on both sides of the wall.",[11,104,105],{},"Which leaves the question that every team adopting these tools eventually reaches. It was never really whether the agents can do more; they can, and they will. The interesting question is where you choose to keep the judgment. We have made our answer fairly explicit, internally and in the product. The agents handle the toil. The decisions stay with us.",[11,107,108,109,113],{},"See what the same discipline does on your deal. ",[87,110,112],{"href":111},"\u002Fcontact","Contact us",".",{"title":115,"searchDepth":116,"depth":116,"links":117},"",2,[118,119,120,121],{"id":23,"depth":116,"text":24},{"id":43,"depth":116,"text":44},{"id":62,"depth":116,"text":63},{"id":78,"depth":116,"text":79},[123],"Pierre Dulac","2026-06-15","How Entropia runs AI agents for reliability, documentation and code, with a human validating every output. Shown at Anthropic's Paris event.",false,"md","\u002Fblog\u002Fdogfood-hero-entropia-anthropic.png",null,{},true,"\u002Fblog\u002Fen\u002Finternal-ai-workflows",{"title":5,"description":125},"blog\u002Fen\u002Finternal-ai-workflows",[136],"insights","VsZ325EWpHN6imYyTo19MDf7gN95PqeBqlvFz_z9LTY",[139,234,512],{"id":140,"title":141,"author":142,"body":143,"coAuthors":219,"date":221,"description":222,"detail":126,"extension":127,"featured":131,"image":223,"imageSize":129,"meta":224,"navigation":131,"path":225,"railTitle":226,"seo":227,"stem":228,"summary":229,"tags":230,"unlisted":126,"updatedAt":129,"video":129,"__hash__":233},"blog\u002Fblog\u002Fen\u002Fentropia-jimini-partnership.md","Entropia connects the data room to Jimini AI","Pierre-Louis Corteel",{"type":8,"value":144,"toc":213},[145,155,159,162,166,169,173,181,185,188,198,206],[11,146,147,148,154],{},"We are pleased to partner with ",[87,149,153],{"href":150,"rel":151},"https:\u002F\u002Fwww.jimini.ai",[152],"nofollow","Jimini AI",", the sovereign legal AI built for law firms and in-house legal teams. Legal professionals can now search, read, analyse or act on their Entropia data rooms directly from Jimini AI.",[21,156,158],{"id":157},"a-new-way-to-run-legal-due-diligence","A new way to run legal due diligence",[11,160,161],{},"Law firms, in-house legal teams and notaries working on M&A transactions spend days in the data room. Until now, to analyse a data room document in their legal AI, they had to download it from the data room first. With the Entropia connector, Jimini AI users can search and read the data room alongside the legal sources they already use in Jimini AI.",[21,163,165],{"id":164},"the-deal-documents-and-the-law-in-one-answer","The deal documents and the law, in one answer",[11,167,168],{},"In a single request, users can query the Entropia data room and legal sources together. They analyse contract clauses and check each one against the applicable law, with citations to the page in the data room and to the legal source. With the Analysis Grid, they apply the same questions to hundreds of data room contracts at once, each cell linked to the passage it came from.",[21,170,172],{"id":171},"entropia-tools-available-from-jimini-ai","Entropia tools, available from Jimini AI",[11,174,175,176,180],{},"The ",[87,177,179],{"href":178},"\u002Fresources\u002Fentropia-mcp-server","Entropia MCP server"," enables Jimini AI's Agent to browse the data room folders, run full-text searches, read documents and spreadsheets, and retrieve the questions and answers exchanged in Q&A threads. A clause to find, a seller's answer in the Q&A, a figure in a spreadsheet: legal teams simply ask Jimini AI.",[21,182,184],{"id":183},"under-the-rights-you-already-hold","Under the rights you already hold",[11,186,187],{},"Every request is initiated by a legal professional, scoped to their access rights in Entropia and logged under their name. Data room administrators decide which groups can use AI. Where the sell side allows it, the bidder's advisers work within their own permissions. Nothing reaches a counterparty from Jimini AI: a person reviews each drafted answer and sends it from Entropia. Both companies keep customer data under European control.",[189,190,191],"blockquote",{},[11,192,193,197],{},[194,195,196],"em",{},"\"Lawyers on a deal need the deal documents and the law side by side. Now they can work on both from the AI platform they have chosen,\""," said Pierre-Louis Corteel, CEO of Entropia.",[189,199,200],{},[11,201,202,205],{},[194,203,204],{},"\"Our ambition is to make Jimini AI the place where legal work gets done, seamlessly connected to the tools legal teams rely on. In M&A, that means bringing the data room directly into their workflow. With Entropia, lawyers can now manage the entire due diligence process within Jimini AI, from reviewing deal documents to drafting the final report. And as two European companies, we share a commitment to keeping sensitive deal data under European control,\""," said Raphaël Arroche, CEO of Jimini AI.",[11,207,208,209,113],{},"Discover Jimini AI at ",[87,210,212],{"href":150,"rel":211},[152],"jimini.ai",{"title":115,"searchDepth":116,"depth":116,"links":214},[215,216,217,218],{"id":157,"depth":116,"text":158},{"id":164,"depth":116,"text":165},{"id":171,"depth":116,"text":172},{"id":183,"depth":116,"text":184},[220],"Raphaël Arroche","2026-11-04","Legal teams can now search, read, analyse and act on their Entropia data rooms directly from Jimini AI, the sovereign legal AI.","\u002Fblog\u002Fjimini-partnership-cover.png",{},"\u002Fblog\u002Fen\u002Fentropia-jimini-partnership","Jimini AI partnership",{"title":141,"description":222},"blog\u002Fen\u002Fentropia-jimini-partnership","Legal teams can now search, read, analyse and act on their Entropia data rooms directly from Jimini AI.",[231,136,232],"solution","changelog","ZMbapJnn_5FTkBkQ6AGv1JLABeKfPZzMIMrcPHUS1cU",{"id":235,"title":236,"author":142,"body":237,"coAuthors":499,"date":502,"description":503,"detail":126,"extension":127,"featured":126,"image":504,"imageSize":129,"meta":505,"navigation":131,"path":506,"railTitle":129,"seo":507,"stem":508,"summary":509,"tags":510,"unlisted":126,"updatedAt":129,"video":129,"__hash__":511},"blog\u002Fblog\u002Fen\u002Fma-tech-circle-paris-september-2026.md","AI in M&A: lessons from Clipperton and Edmond de Rothschild",{"type":8,"value":238,"toc":484},[239,242,245,249,253,257,263,266,272,275,278,281,285,288,291,296,301,304,307,310,314,319,322,325,329,332,373,377,382,385,388,391,398,402,407,410,413,416,420,425,428,431,434,437,441,446,449,452,456,459,462,466,469,477],[11,240,241],{},"The M&A Tech Circle, hosted by Entropia, brings M&A professionals together every quarter to explore how technology is transforming the profession, through real cases and first-hand accounts.",[11,243,244],{},"Here is our recap of the Paris edition, held on 23 September 2026 at Entropia's offices.",[246,247],"blog-gallery",{":images":248},"[{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Froom-1.jpg\",\"alt\":\"The M&A Tech Circle room (1\u002F4)\",\"width\":2419,\"height\":3226},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Froom-2.jpg\",\"alt\":\"The M&A Tech Circle room (2\u002F4)\",\"width\":4898,\"height\":3265},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Froom-3.jpg\",\"alt\":\"The M&A Tech Circle room (3\u002F4)\",\"width\":3265,\"height\":4898},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Froom-4.jpg\",\"alt\":\"The M&A Tech Circle room (4\u002F4)\",\"width\":1348,\"height\":899}]",[250,251],"section-label",{"label":252},"Announcements",[21,254,256],{"id":255},"entropias-partnerships-with-claude-anthropic-rogo-and-model-ml","Entropia's partnerships with Claude (Anthropic), Rogo and Model ML",[11,258,259,260],{},"The evening opened with a look back at the partnerships Entropia recently signed with the leading AI platforms used by investment banks. Entropia was the first data room in the world with a native connector in Claude, Rogo and Model ML. M&A teams can connect Entropia to most AI platforms on the market: every one that supports MCP (Model Context Protocol). ",[87,261,262],{"href":178},"Learn more",[11,264,265],{},"It was also a chance to spell out Entropia's differentiation strategy. Some data room market leaders sell their own AI layer as a paid add-on and deliberately limit what their MCP server can do, when they have one at all. Yet their clients have already chosen their AI platform, built their skills on it and connected their sources. And even after paying for the add-on, they still lack essential tools, such as reading the full text of the documents in the data room.",[11,267,268,269],{},"Entropia sided with its clients: from their own AI platform, their teams work on the data room with far more complete tools, full document reading included, at no extra cost. ",[87,270,262],{"href":271},"\u002Fblog\u002Fmost-capable-data-room-mcp",[11,273,274],{},"Those capabilities make all the difference for teams that work with AI every day.",[11,276,277],{},"That is the case for the M&A teams at Edmond de Rothschild, who rolled out Rogo, and at Clipperton, who work with Claude. Manon Elbaz and Olivier Combaudou then took the stage to talk through both rollouts.",[250,279],{"label":280},"Panel",[21,282,284],{"id":283},"edmond-de-rothschild-x-clipperton","Edmond de Rothschild x Clipperton",[11,286,287],{},"Manon Elbaz, Chief of Staff at Edmond de Rothschild, and Olivier Combaudou, Partner at Clipperton, both lead the AI rollout at their bank. They came to share how it is going.",[246,289],{":images":290},"[{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fpanel-1.jpg\",\"alt\":\"The panel with Manon Elbaz and Olivier Combaudou (1\u002F5)\",\"width\":557,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fpanel-2.jpg\",\"alt\":\"The panel with Manon Elbaz and Olivier Combaudou (2\u002F5)\",\"width\":557,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fpanel-3.jpg\",\"alt\":\"The panel with Manon Elbaz and Olivier Combaudou (3\u002F5)\",\"width\":1254,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fpanel-4.jpg\",\"alt\":\"The panel with Manon Elbaz and Olivier Combaudou (4\u002F5)\",\"width\":618,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fpanel-5.jpg\",\"alt\":\"The panel with Manon Elbaz and Olivier Combaudou (5\u002F5)\",\"width\":624,\"height\":936}]",[292,293,295],"h3",{"id":294},"how-to-choose-an-ai-platform","How to choose an AI platform",[11,297,298],{},[194,299,300],{},"Choices that are anything but final.",[11,302,303],{},"Edmond de Rothschild uses Rogo, a platform specialised in financial analysis. Manon Elbaz detailed the criteria that tipped the balance. AI models move very fast, and Rogo lets teams switch from one to another without changing tools.",[11,305,306],{},"Clipperton built its own environment, with Claude as its main platform, and handles orchestration, connections and skills itself.",[11,308,309],{},"Both stressed that these choices are anything but final and may well change, because the bulk of the work does not lie in picking the platform. After all, if every bank works with the same tool, how do you stand out? For Clipperton, the first step was to write down its processes and its knowledge base.",[292,311,313],{"id":312},"rolling-out-ai-in-an-ma-team-what-takes-time","Rolling out AI in an M&A team: what takes time",[11,315,316],{},[194,317,318],{},"Structuring knowledge, making methods explicit, training the teams.",[11,320,321],{},"At Clipperton, the time went into the conditions of use more than into the technology. The biggest workstream is still the knowledge base: the partners wrote down methods, guidelines and processes that had never been formalised. That pushed them to think more explicitly about what makes them distinctive. The team also drafted a charter, and one rule sums up its spirit: \"The AI did it\" is never an excuse.",[11,323,324],{},"At Edmond de Rothschild, Manon Elbaz described a pilot phase in which the team is learning fast. The time goes into training, so people learn to use the tool and to challenge it, into defining the framework of use, into tracking usage and into working with internal \"champions\". Her goal is to measure how each person uses it, so that AI stays in the service of judgement. Usage varied widely at first. Today the whole team uses it, and real power users have emerged among the analysts. The challenge now is to make usage consistent.",[292,326,328],{"id":327},"the-ai-use-cases-that-work-in-investment-banking","The AI use cases that work in investment banking",[11,330,331],{},"On use cases, Manon Elbaz and Olivier Combaudou agreed on the essentials. Their teams already use AI every day across many use cases:",[333,334,335,343,349,355,361,367],"ul",{},[336,337,338,342],"li",{},[339,340,341],"strong",{},"Sourcing and buyer identification",": quickly finding prospects, potential buyers and sector data.",[336,344,345,348],{},[339,346,347],{},"Sparring",": challenging an approach, exploring scenarios, finding angles nobody had thought of.",[336,350,351,354],{},[339,352,353],{},"Knowledge management",": building knowledge bases on specific topics. One example at Clipperton is comparables: building lists of multiples to use as benchmarks.",[336,356,357,360],{},[339,358,359],{},"First drafts",": bankers still write the final version of an investment thesis themselves, but save significant time on the first one.",[336,362,363,366],{},[339,364,365],{},"Checking",": spotting typos and inconsistencies between the sections of a document.",[336,368,369,372],{},[339,370,371],{},"Q&A",": preparing answers to buyers' questions. Olivier Combaudou walked through Clipperton's setup, the tools connected to Claude and the in-house skills, then the Q&A skill applied to a recent mandate. The whole team uses it.",[292,374,376],{"id":375},"what-banks-dont-want-to-automate","What banks don't want to automate",[11,378,379],{},[194,380,381],{},"You can ask AI to write an IM, but you shouldn't.",[11,383,384],{},"Both speakers draw the same line. Negotiations and direct exchanges with clients stay entirely human: the relationship, the nuance and reading what goes unsaid cannot be delegated.",[11,386,387],{},"As for automating key deliverables such as the IM, the tests have not been conclusive. Bankers get generic text, while the IM is one of the documents where a bank most wants to stand out. The variety of sources and formats also makes the AI's job harder.",[11,389,390],{},"Olivier Combaudou pointed to the difficulties that come with the nature of Clipperton's mandates, mostly with tech companies, where you price a vision more than an EBITDA multiple.",[11,392,393,394,113],{},"Automating deliverables is probably more within reach for mature companies in standardised markets, such as the accounting-firm build-up Aesus Advisory presented at ",[87,395,397],{"href":396},"\u002Fblog\u002Fma-tech-circle-paris","the previous edition of the M&A Tech Circle",[292,399,401],{"id":400},"how-to-measure-the-roi-of-ai-in-ma","How to measure the ROI of AI in M&A",[11,403,404],{},[194,405,406],{},"Ask a better question.",[11,408,409],{},"The ROI of AI is still hard to put a number on. You can get a rough idea by dividing the annual cost by the number of pitches and mandates. You can try to estimate the time saved per person and per grade, but a complete costed simulation is not realistic at this stage. The cost of AI is far from neutral and keeps rising, current prices probably don't reflect the real cost of these tools, and nobody knows what they will be in six or twelve months.",[11,411,412],{},"Above all, it isn't really the right question to ask for now. Nobody measured the ROI of the internet or of laptops overnight, but everyone saw the way people work change profoundly.",[11,414,415],{},"M&A processes will evolve and teams will work differently. Both speakers agreed that the best bankers will pull further ahead thanks to AI. The point is to raise the quality of deliverables, not so much productivity. The ROI will become clear over time.",[292,417,419],{"id":418},"will-ai-reduce-junior-hiring-in-investment-banking","Will AI reduce junior hiring in investment banking?",[11,421,422],{},[194,423,424],{},"No, but it changes what is expected of them.",[11,426,427],{},"The short answer: nothing points that way today. Neither bank has changed its hiring.",[11,429,430],{},"Edmond de Rothschild is still observing. For Manon Elbaz, even as AI lifts every level, interns must keep mastering the fundamentals on real cases: building a business plan, a DCF, a valuation, calculating a WACC. A junior who has never built a DCF by hand cannot challenge the AI, just as a manager must know how to do what their team does.",[11,432,433],{},"Clipperton is not cutting its hiring at all, but it is looking for slightly different profiles, and its expectations of juniors have risen markedly. Olivier Combaudou doesn't want \"prompt operators\" but analysts who can articulate a line of thought. He values new skills: in the past, a tedious task done well was enough, and the senior brought the thinking afterwards. Once that task can be automated, the bank expects the junior to deliver a first layer of added value.",[11,435,436],{},"The question is how to use AI to augment every member of the bank, not how to automate the interns' work. Comparing an analyst's productivity with a model's only holds for certain tasks. And a bank that hired fewer juniors would deprive itself of its future associates, managers and partners.",[292,438,440],{"id":439},"the-investment-bankers-job-in-ten-years","The investment banker's job in ten years",[11,442,443],{},[194,444,445],{},"The bank's added value doesn't change.",[11,447,448],{},"What is becoming a commodity is the exploration and analysis of large amounts of information: the first layer of market analysis, summaries of public data, data structuring. Differentiation will therefore come down to judgement, vision, the ability to ask the right questions and see what others miss, and knowledge of markets, sectors and players.",[11,450,451],{},"The job stays competitive: a client comes looking for the best deal and the right investor. An investment bank's deliverables are a means to that end, not the end itself. If part of the work becomes a commodity, the bank's added value doesn't change. Bankers will be able to spend more time advising, and the bank will be able to take more risks. Execution speeds up, but deciding what to do with it remains an in-house craft. And AI only works on part of a deal's raw material: the data, provided it is well organised!",[21,453,455],{"id":454},"cocktails","Cocktails",[11,457,458],{},"The conversations went on late into the evening, sparking many introductions and plenty of new projects.",[246,460],{":images":461},"[{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-1.jpg\",\"alt\":\"Cocktails after the panel (1\u002F6)\",\"width\":1105,\"height\":737},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-2.jpg\",\"alt\":\"Cocktails after the panel (2\u002F6)\",\"width\":557,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-3.jpg\",\"alt\":\"Cocktails after the panel (3\u002F6)\",\"width\":557,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-4.jpg\",\"alt\":\"Cocktails after the panel (4\u002F6)\",\"width\":557,\"height\":836},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-5.jpg\",\"alt\":\"Cocktails after the panel (5\u002F6)\",\"width\":624,\"height\":936},{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fcocktail-6.jpg\",\"alt\":\"Cocktails after the panel (6\u002F6)\",\"width\":1348,\"height\":899}]",[21,463,465],{"id":464},"in-the-room","In the room",[11,467,468],{},"A big thank you to everyone who joined us:",[470,471,474],"callout",{"color":472,"icon":473},"neutral","i-lucide-users",[11,475,476],{},"2A Capital · ABN Amro · Adviso Partners · Amala Partners · Arcéane · Ardian · Atout Capital · BNP Paribas · Bucéphale Finance · CA CIB · Cambon Partners · Clipperton · D&A Corporate Finance · Edmond de Rothschild · Eight Advisory · EY M&A Advisory · EY Parthenon · Forvis Mazars · Fournel Advisory · Groupama · in Extenso · Indosuez CF · Inflection Point Technology Partners · Investec · Klutch Capital · Largillière Finance · Leuwen Euroland · M&A Advisor · Mansartis · Maupiti Partners · Mazarine Partners · MBA Capital · Priscus Finance · Raisers Partners · Rochefort & Associés · Rydge Conseil · Sanofi · Spayne Lindsay · Tevali Partners · TTS Group · Training You · Trialis · Windome Banking",[478,479],"matc-credits",{":entropiaHeight":480,":partners":481,"entropiaHref":482,"label":483},"19","[{\"src\":\"\u002Fblog\u002Fmatc-202609\u002Fclipperton-logo-dark.png\",\"alt\":\"Clipperton\",\"href\":\"https:\u002F\u002Fwww.clipperton.net\u002F\",\"height\":14},{\"src\":\"\u002Fcustomers\u002Fedmond-de-rothschild.png\",\"alt\":\"Edmond de Rothschild\",\"href\":\"https:\u002F\u002Fwww.edmond-de-rothschild.com\u002F\",\"height\":56}]","\u002F","Organized by",{"title":115,"searchDepth":116,"depth":116,"links":485},[486,487,497,498],{"id":255,"depth":116,"text":256},{"id":283,"depth":116,"text":284,"children":488},[489,491,492,493,494,495,496],{"id":294,"depth":490,"text":295},3,{"id":312,"depth":490,"text":313},{"id":327,"depth":490,"text":328},{"id":375,"depth":490,"text":376},{"id":400,"depth":490,"text":401},{"id":418,"depth":490,"text":419},{"id":439,"depth":490,"text":440},{"id":454,"depth":116,"text":455},{"id":464,"depth":116,"text":465},[500,501],"Manon Elbaz","Olivier Combaudou","2026-09-23","Clipperton and Edmond de Rothschild on their AI rollouts: their setups, what works, what takes time. ROI, the impact on staffing, and where the job is heading.","\u002Fblog\u002Fma-tech-circle-paris-202609.png",{},"\u002Fblog\u002Fen\u002Fma-tech-circle-paris-september-2026",{"title":236,"description":503},"blog\u002Fen\u002Fma-tech-circle-paris-september-2026","Clipperton and Edmond de Rothschild on their AI rollouts in M&A.",[136],"zt0P3BZtR3s6mzIU4VCukGs4nwFjha6syVSRNrrog3U",{"id":513,"title":514,"author":6,"body":515,"coAuthors":556,"date":558,"description":559,"detail":126,"extension":127,"featured":131,"image":560,"imageSize":129,"meta":561,"navigation":131,"path":562,"railTitle":563,"seo":564,"stem":565,"summary":566,"tags":567,"unlisted":126,"updatedAt":129,"video":129,"__hash__":568},"blog\u002Fblog\u002Fen\u002Fentropia-model-ml-partnership.md","Entropia's Partnership with Model ML",{"type":8,"value":516,"toc":554},[517,527,530,537,540,543,551],[11,518,519,520,526],{},"We are pleased to announce a partnership with ",[87,521,525],{"href":522,"rel":523,"target":524},"https:\u002F\u002Fmodelml.com",[152],"_blank","Model ML",", the agentic operating system for financial services, used by some of the world's largest banks, asset managers and advisory firms.",[11,528,529],{},"Model ML builds custom and off-the-shelf agents which create, review and verify complex, document-heavy workflows for global investment banks, private equity houses, consulting firms and other major financial institutions.",[11,531,532,533,536],{},"Entropia was designed to support M&A professionals with AI, and that design extends beyond our own walls. A user connected from Model ML gets ",[87,534,535],{"href":178},"the same tools"," as Entropia's own AI, at the same depth.",[11,538,539],{},"Bankers working in Model ML can reach their Entropia data rooms without leaving it. From Model ML, a banker can search the data room, read documents, presentations and spreadsheets page by page, browse the folder structure, follow Q&A threads, check who has access to what, and act: create folders, tag documents, set permissions, draft Q&A answers that a human reviews and sends.",[11,541,542],{},"Each request is initiated by the user, scoped to the access rights they already hold, and logged in the data room audit trail under their name. Nothing reaches a Model ML user that they could not open in Entropia directly, and nothing reaches a counterparty from Model ML: a drafted answer is reviewed and sent by a human in Entropia, and the access of an active group in the data room cannot be changed from outside it.",[189,544,545],{},[11,546,547,550],{},[194,548,549],{},"\"Our partnership with Entropia is a meaningful addition to the Model ML platform for our M&A users. Access to Entropia's agentic data rooms directly in the Model ML platform means that our users can harness the full power of Entropia's agent-native platform without disrupting their existing workflows,\""," said Chaz Englander, CEO at Model ML.",[11,552,553],{},"The integration is available to joint customers with active Entropia data rooms, starting today.",{"title":115,"searchDepth":116,"depth":116,"links":555},[],[557],"Chaz Englander","2026-09-10","Bankers working in Model ML can now search, read and act on their live Entropia data rooms without leaving the platform, under their own access rights.","\u002Fblog\u002Fmodel-ml-partnership-cover.png",{},"\u002Fblog\u002Fen\u002Fentropia-model-ml-partnership","Model ML partnership",{"title":514,"description":559},"blog\u002Fen\u002Fentropia-model-ml-partnership","Bankers working in Model ML can now reach their live Entropia data rooms without leaving the platform.",[231,136,232],"RrYTYx7Q0M0_Gs5IBSEzgqXbk-fckPrjcrUqIT3yuA8",1794484452888]