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Today’s Rundown

• AI prompts for searchers

• How to build a vertical SaaS empire

• 96% pass rate of search fund investor

• 2 deal / launch announcements

Database Overview

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Weekly Highlights

• Chris von Wedemeyer, founder of search fund investor Legacy Partners published a piece on AI prompts to reduce weak underwriting:

  • LLMs add the most underwriting value when used with tightly scoped, context-rich prompts that force uncertainty, separate facts from assumptions, and produce concrete artifacts rather than generic summaries

  • The highest leverage use cases concentrate on three stages of ETA work choosing the right acquisition lane, building a realistic target universe, and preparing first owner calls that surface fragility early instead of confirming optimistic teasers

  • Well-designed prompts consistently compress low-value thinking and drafting so searchers can reallocate time toward owner conversations and downside underwriting, which remain the primary drivers of deal outcomes

  • Treating every claim as unproven unless tied to benchmarks, falsifiers, or documents materially reduces weak underwriting and helps identify single points of failure such as customer concentration, key-person risk, or fragile leverage before LOI

  • Running the same prompt across multiple models and analyzing disagreements as signals of thin inputs or hidden assumptions improves decision quality at low cost

  • View his prompts in the article

• Verticals published a new podcast episode on how to build a vertical SaaS empire with Sam Youssef, CEO of Valsoft:

  • Valsoft is positioned as a Constellation-style vertical software acquirer operating at large scale with ~3,000 employees, ~$700M+ revenue, 130+ acquisitions, and a portfolio strategy focused on durable, mission-critical systems of record

  • Their target size band concentrates on ~3M to ~100M revenue companies to reduce portfolio concentration risk while still supporting meaningful R&D, and they underwrite entry pricing across a wide range from ~1x ARR up to ~10x ARR based on product quality, tech debt, and cross-sell potential

  • Value creation is described as repeatable and operationally driven with ~7 integration playbooks including payments infrastructure, AI widgets, and heavy product reinvestment via global delivery centers that can add ~20–25 developers to modernize products and accelerate feature velocity

  • A flagship example highlights operating leverage from execution where an initial ~$5M acquisition grew from ~1M+ EBITDA to ~3M EBITDA in ~18 months and later to ~$10M+ EBITDA, with the broader hospitality suite reaching ~$50–60M revenue from a core base of thousands of customers

  • Portfolio outcomes are framed as asymmetric with ~25–35% of acquisitions landing below expectations due to factors like low-integrity sellers or customer concentration losses, while the majority outperform expectations, supported by a dedicated sourcing engine of 100+ deal professionals across ~25 countries and a view that AI increases both opportunity and risk primarily for legacy, high tech-debt products that cannot iterate quickly

• Grant Hensel, an entrepreneur and investor, shared why he passed on 114 out of 119 ETA deals in 2025:

  • The dataset implies a ~96% pass rate and only ~4% advancing beyond the investor’s filter

  • Searcher risk accounts for ~17% of passes and clusters around post-close leadership fit, missing functional skill coverage like B2B sales, coachability and temperament signals, plus practical constraints like not qualifying for an SBA loan

  • Business risk accounts for ~19% of passes and is dominated by “default risk” fragility factors such as top-1/2 customer concentration, high CapEx intensity that makes EBITDA an unreliable proxy for true cash yield, cyclicality tied to construction or discretionary spend, and seller key-person dependence

  • Deal terms drive the largest share at ~32% of passes and the pattern is pricing plus structure, meaning valuation unattractive versus alternatives and insufficient downside protection such as minimal seller note or other risk-sharing mechanics

  • Investor terms and deal size are meaningful but secondary with ~9% and ~19% respectively, suggesting that mispriced capital terms are less common than fundamental fit issues, while mandate fit on size is a frequent mechanical filter comparable in weight to business risk

Deal / Launch Announcements

• 🇮🇹 Solferino Capital, a search fund managed by Francesco Revel-Sillamoni, acquired Tanks International, a industrial packaging business (link)

• 🇪🇸 Divisadero Capital, a search fund managed by Fernando Garcia-G Benavides, acquired VisualCounter, a precision-analytics company (link)

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