> ## Content Index > Fetch the complete content index at: https://intelligence.artificialrealitymedia.com/llms.txt > Use this file to discover other available public pages before exploring further. # Inside the deal room an M&A broker duct-taped together - URL: https://intelligence.artificialrealitymedia.com/ma-deal-infrastructure-built-in-house/ - Published: 2026-09-02T12:07:00.000Z - Updated: 2026-09-02T12:07:00.000Z - Description: Christine McDannell takes five deals at a time and reports a seven-month close on infrastructure she assembled herself. What she wants built instead. - Author: Galina Fendikevich - Tags: M&A advisory, #For AI Companies, #Product requests, #Import 2026-09-03 13:12 Christine McDannell, founder of The Magnolia Firm, a sell-side M&A brokerage in Austin that sells profitable technology companies, runs her transactions on deal infrastructure she assembled herself: a valuation skill trained on her own closed deals, an adverse-information sweep, and a data room built by hand. She takes five deals at a time and reports an average close of seven months, against an industry average she estimates at twelve to eighteen. She told Artificial Reality, the AI buyer-evidence podcast hosted by Galina Fendikevich, that she would rather buy the thing she built. No vendor product was named for this workflow, and that absence is the finding. ## What does her deal process actually look like? The sequence at The Magnolia Firm is largely standard for sell-side M&A, and McDannell walks it in order: valuation first, then diligence, then packaging, then market. > "We start with \[a\] valuation right out the gate. And so that gives us a starting point to make sure they're comfortable with that. I never want them to exit for a price that they don't want to exit at." > "We do all the due diligence up front. I don't want to find anything at the very end of a transaction that blows it up. So I do a very deep due diligence. Then we package them, we have the narrative, we have a seller interview we do, we put that in the data room — so a very comprehensive data room — and then we take it to market." The diligence is deliberately heavier than the buyer's, and it happens before she signs the client: > "I do deeper due diligence than the buyers do, up front, before I take a client ... I'm looking at the [Instagram](https://www.instagram.com/?ref=intelligence.artificialrealitymedia.com) comments, make sure there's not complaints. I'm combing through everything." ## Where does AI sit inside that sequence? In the research-heavy steps, built as skills rather than bought as a platform. McDannell describes the adverse-information sweep she runs on every prospective client: > "A skill to then tell it to scrape every single thing on this owner and every single thing on this business. I want no surprises — lawsuits, comments, et cetera. Anything bad I need to know up front so that we can figure out the objection, because people are gonna find it otherwise." Her valuation skill draws on her own past deals and, by her own assessment, gets roughly 80% of the way there. Sensitive material is handled with a manual control: her firm strips company names before submitting financials to any model. ## What did she ask builders for? The whole thing, as a product. Asked whether she would want the infrastructure she has assembled to exist rather than be rebuilt firm by firm, McDannell said: > "In M&A specifically, this whole infrastructure — that would be rad. That would be rad, because it is very tailored, like what we've built out." Fendikevich's framing, which McDannell endorsed: the data room preparation, the first valuation, the seller interview and the buyer outreach are standard across M&A, with only business logic and data needing adjustment per firm. That is the generalisation claim a builder would be betting on, and it comes from the operator rather than from Artificial Reality. ## What would the product have to do? Stated by the operator: - **Cover the standard sell-side sequence** — valuation, deep due diligence, packaging and narrative, seller interview, data room, buyer outreach, buyer qualification. - **Encode industry knowledge and current market conditions** rather than requiring each firm to supply them. - **Discover adverse information across public sources**, including social comments and litigation. - **Stay configurable**, since her valuation skill draws on her own past deals. - **Protect sensitive financials**, which is why her firm strips company names before anything reaches a model. Artificial Reality adds three. Get valuation right or refuse to answer, because a confident number handed to a once-in-a-lifetime seller is this category's worst failure. Support buyer qualification rather than replacing it. And keep the firm's deal history portable, because accumulated comparables are what makes each subsequent valuation better. ## What would she not hand to software? The buyer gate, which she flags as unusual and treats as her differentiator: > "We don't let anybody in that data room ... we have to have an NDA and an actual phone call with every single potential buyer. And I don't see any broker doing that, to be honest." A platform that helpfully automated that step would be selling her the erosion of her own advantage. Worth reading as a general signal: in a market where AI compresses every process it touches, the deliberate human step is what a firm has left to differentiate on. ## What a builder should take from this A boutique brokerage running five live transactions on self-assembled tooling has already specified the product and priced it in her own evenings. The economics explain why nobody has shipped it — there are many small sell-side firms and none of them is a large enough account on its own — and the explanation does not make the gap less real. So the question for anyone building here is narrower than it looks: can your system say "I don't know" about a valuation, and would your buyer trust you more if it did? ## Where this comes from S1E5: How she cut her payroll by 83% using one AI agent — the full interview with Christine McDannell. Listen or watch: [YouTube](https://www.youtube.com/watch?v=1VO7nWtzNEU&ref=intelligence.artificialrealitymedia.com), [Spotify](https://open.spotify.com/episode/6E4sAUyfk4X5gUqEtzhL8o?ref=intelligence.artificialrealitymedia.com) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/how-she-cut-her-payroll-by-83-using-this-one-ai-agent/id6780699025?i=1000776953676&ref=intelligence.artificialrealitymedia.com).