There's a strange tension at the heart of modern dealmaking. Investment banks orchestrated roughly $5 trillion in mergers and acquisitions last year—a staggering volume by any measure—yet much of the actual execution still happens the way it did a decade ago. Junior bankers hunched over Excel. Associates cross-checking page 47 of a pitch deck against page 12 of a financial model. Partners emailing marked-up PowerPoints at 2 a.m.
Maywood, a fledgling startup that emerged from Y Combinator in early February, believes it has found a wedge into this world. The company's pitch is straightforward, if ambitious: automate the grunt work of M&A deal execution—from initial financial upload to final close—without sacrificing the meticulous polish that bankers (and their clients) expect.
It's a crowded promise. The market for M&A workflow software has become something of a gold rush in the past 18 months, with well-funded competitors like Rogo raising $75 million and legacy data room providers bolting AI onto existing platforms. But Maywood's three founders—who came up through Blackstone, BCG, and Balyasny—think they've spotted an opening that others have missed.
Whether they're right may determine whether a two-person team can carve out territory before better-capitalized rivals close the gap.
A Living Document, Not Just a Generator
At its core, Maywood offers what it calls a "living workspace" for deal teams. The concept sounds simple enough: upload financial statements, management presentations, data room files. The platform then spins out confidential information memoranda, pitch decks, teasers, and internal memos, all formatted in a firm's house style.
What makes it more than a glorified template engine, according to the company, is synchronization. When underlying data shifts—a revised revenue forecast, an updated customer list, a tweaked valuation multiple—those changes cascade automatically through every linked document. No more hunting through 14 PowerPoint versions to find which deck still has last quarter's numbers.
The platform tackles three layers: presentation generation, due diligence management, and company research. On the diligence front, it surfaces answers to buyer questions, flags gaps in the data room, and identifies potential red flags before they become deal-breakers. For research, it pulls from files, emails, meeting notes, data vendors, and internal databases to generate comparable company analyses, competitive landscapes, and industry overviews.
One early testimonial, cited in Maywood's launch materials, claims a deal team "doubled capacity" and now expects to "double deal count with the same headcount." That's the kind of efficiency gain that gets managing directors' attention. Perhaps more than the founders initially expected.
The Founders' Bet
Drake Goodman, Maywood's CEO, spent time at Blackstone after earning dual degrees from Wharton's Huntsman Program. His co-founder Kent Goodman (no relation publicly disclosed) came through BCG and MIT. The third co-founder, CTO Esteban Vizcaino, logged years at Balyasny Asset Management following computer science studies at MIT.
Their shared diagnosis: M&A workflows remain stubbornly "fragmented and manual," with junior talent burning hours reconciling data scattered across spreadsheets, slides, and email threads. Maywood's approach eliminates that reconciliation layer entirely—or tries to, anyway.
Crucially, the founders position their output not as a finished product but as draft-quality material that dealmakers can refine. This is editability over automation, auditability over black-box magic. In a December blog post, Maywood sketched what it called "The AI Investment Banking Associate of 2027"—a vision where AI regenerates CIM sections on command, updates models when assumptions shift, and chains multi-step workflows without constant prompting.
The subtext isn't hard to read. This isn't about replacing banker judgment. It's about removing mechanical friction that keeps talented professionals from doing higher-value work. Whether banks buy that framing is another question.
A Very Crowded Field
Maywood's timing is deliberate, but it's hardly alone.
Rogo, perhaps the most visible player in this space, raised a $75 million Series C from Sequoia in January 2026, following a $50 million Series B just nine months earlier. The company positions itself as an "AI analyst" and counts Moelis and Lazard among its users—names that carry weight in this industry.
Hebbia, another well-funded contender, claims to automate up to 90 percent of finance and legal work through what it calls "agentic workflows." Its Matrix product handles pitch generation, CIM creation, and diligence acceleration, with a focus on deep document retrieval and intelligence.
Then there's the data room layer. Datasite—which won "M&A Technology of the Year" in 2024 and acquired AI sourcing platform Grata for north of $200 million last June—has been steadily rolling out AI features including automated redaction and intelligent search. SS&C Intralinks introduced its DealCentre AI capabilities in late 2025, redesigning Q&A workflows and adding AI-powered navigation across virtual data rooms.
Beyond point solutions, large consulting firms are building their own M&A AI stacks. PwC partnered with Harvey to deploy an AI platform across its global network, logging more than 10,000 executions on a single diligence workflow. Meanwhile, banks including BNP Paribas and Goldman Sachs have deployed internal AI assistants to accelerate pitch creation and market analysis.
Maywood's bet—and it is a bet—is that none of these offerings quite nail the full execution workflow for middle-market and large investment banks running multiple sell-side processes annually. The company's differentiation hinges on that "living workspace" concept and on learning firm-specific style from historical CIMs and pitches. Whether that's enough of a moat remains to be seen.
The Technical Stack (What They'll Share)
Maywood offers single-tenant deployment by default, with a self-hosted option allowing clients to run the platform inside their own cloud infrastructure. The company has secured SOC 2, GDPR, and CCPA compliance. ISO 27001 and ISO 42001 certifications are listed as "in process."
Which large language models power the system? That remains undisclosed, though the company's website references "API models" in its architecture documentation. With just two team members listed on Y Combinator's jobs page as of late February, Maywood is actively hiring Applied AI Engineers and AI Researchers at $150,000 to $250,000 salaries in New York and San Francisco.
Pricing isn't public. Neither are customer names, though the company says it's "working with several banks" and that some managing directors are forwarding Maywood-generated materials directly to clients—a claim that, if true, signals considerable confidence in output quality. Or perhaps just early-adopter risk tolerance.
The Market Is Already Moving
Here's the thing: Maywood isn't pitching a speculative future. It's selling into a market that has already begun restructuring how junior-level work gets done.
Adoption data from Bain suggests the broader M&A industry is warming to generative AI faster than many expected. In a February 2025 report, Bain found that roughly 21 percent of surveyed companies were already using genAI in M&A processes, with more than half expecting full integration by 2027. A year earlier, that usage figure sat at 16 percent, with expectations that adoption would hit 80 percent over three years.
Financial News London reported in early 2026 that jobs "that used to exist don't today" as dealmakers accelerate their AI push. The publication cited Goldman Sachs, UBS, Nomura, and Moelis as active adopters or partners in various AI initiatives. Goldman rolled out its AI Assistant to roughly 10,000 employees by mid-2025, covering investment banking and wealth management workflows.
The implication is clear. This market is live. The question isn't whether banks will adopt AI for M&A execution—many already have. The question is whether a two-person startup can move fast enough to capture meaningful share before better-capitalized competitors or internal bank platforms squeeze the window shut.
What Happens Next
For now, Maywood is focused on proving the concept with its unnamed early customers and refining the product ahead of Y Combinator's Demo Day on March 24, 2026. The founders are betting that the testimonials they've collected—doubled capacity, doubled deal count—will resonate with managing directors hunting for competitive advantage in an increasingly automated market.
If they're right, investment banks may discover that the real edge in 2026 isn't the size of their associate pool but the sophistication of their execution infrastructure. If they're wrong, Maywood becomes another promising footnote in the ongoing automation of Wall Street's middle office.
Either way, the paradox that opened this story remains. Billions in deal volume. Manual execution. Something has to give. Whether Maywood is the one to crack it—or just one more entrant in a race that's already been won elsewhere—will become clear soon enough.
