How F2 hires only ex-finance professionals for sales instead of traditional salespeople | Donald Muir

F2 is the AI platform for private markets investors, automating due diligence and portfolio monitoring workflows with agentic AI. After building ARK into a digital banking platform that scaled from tens of millions to tens of billions in loan volume, Donald Muir developed AI technology to automate debt placement on ARK's marketplace. When upmarket institutional lenders requested access to the AI for their entire deal flow—not just ARK's marketplace deals—Donald recognized the technology's standalone value. In this episode of BUILDERS, Donald shares how he's commercializing enterprise-grade AI for an industry where he personally spent years in the private equity bullpen, and how F2 is addressing the reliability and trust barriers that prevent AI adoption in high-stakes financial decision-making. Topics Discussed How F2 emerged from ARK's internal need to automate debt marketplace screening memos The technical approach to eliminating hallucination in Excel-based financial analysis Replicating private equity's "super day" interview format to prove AI capability with live deal data Sales team composition: hiring ex-finance professionals instead of traditional sales reps AI's role in evolving private equity analysts from menial tasks to system operators Product roadmap from due diligence to portfolio monitoring to deal syndication platform Maintaining operational independence while preserving strategic alignment with ARK GTM Lessons For B2B Founders Solve your own hardest problem first, then productize: Donald built F2's core technology to scale ARK's debt marketplace, focusing on the most difficult engineering challenge—reliable financial analysis of unstructured Excel data—because the marketplace required it. This resulted in technology that foundation models still haven't replicated over a year later. The aha moment came when institutional lenders wanted the AI for all their deal flow, not just marketplace transactions. Organic internal development created category-leading capabilities and validated product-market fit before commercialization. B2B founders should identify which internal operational challenges, if solved, could become standalone products serving the broader market. Design sales processes that mirror how your ICP evaluates talent: Donald replicated private equity's "super day" format where analyst candidates receive a data room, laptop without internet access, and three hours to produce an LBO model and investment thesis. F2 runs identical timed tests—customers send live deal data rooms under NDA, F2 generates investment committee memos using their templates, and presents same-day results. This proves the AI can perform at the standard funds use to evaluate human analysts they hire 18 months before start dates. B2B founders selling into industries with rigorous talent evaluation processes should reverse-engineer those frameworks into product demonstrations that speak to buyer expectations. Prioritize credibility over sales experience in technical markets: Donald's entire sales team consists of ex-finance professionals who lived in the seat—no traditional salespeople. These reps can screen-share investment memos created that morning and discuss them authentically with MDs and principals using industry-specific language. After 4.5 years running go-to-market at ARK, Donald teaches sales methodology to domain experts rather than teaching domain expertise to salespeople. For deals averaging half a billion dollars flowing through the platform, buyer credibility outweighs sales polish. B2B founders in specialized verticals should evaluate whether domain fluency or sales pedigree matters more for their specific buyer personas and deal complexity. Engineer for auditability before optimizing for speed: F2 focused on eliminating hallucination and achieving mathematical accuracy—solving what Donald calls the "reliability and trust" gap—before addressing workflow efficiency. The company name references the F2 keystroke used to audit Excel calculations at 3 AM in the PE bullpen. This positioning directly addresses the barrier preventing AI adoption for investment decisions: LLMs hallucinate, can't do math, and lack auditability. Only after proving the AI produces auditable, trustworthy output did F2 layer on speed benefits. B2B founders building for high-stakes decision environments should identify the fundamental trust barrier and make it the core technical focus before feature expansion. Leverage institutional knowledge as competitive differentiation: Beyond automating existing workflows, F2 enables firms to pipe in decades of institutional knowledge via API—instantly benchmarking new deals against thousands of historical transactions by vertical, revenue size, leverage levels, and management quality. This transforms screening memos from isolated analyses into context-rich evaluations informed by complete firm history. The AI doesn't just work faster; it has comprehensive context that individual analysts manually searching SharePoint folders could never access. B2B founders should identify where accumulated institutional data creates compounding value beyond point-in-time automation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM