https://markrichman.com/assets/Engagements-CCzBxnZN.js

1import{j as e,L as t}from"./index-Ly5--8PG.js";import{S as r}from"./SEO-BID_fApt.js";import{B as l,I as d}from"./StructuredData-jophOexY.js";const s=[{slug:"healthcare-saas-platform-acquisition",engagement:"Diligence → Value Creation",sector:"Healthcare SaaS",scale:"$45M ARR",headline:"A $5M price reduction at signing became $16M of value creation in year one",metrics:[{value:"31%",label:"cloud spend reduction in year one"},{value:"$16M",label:"value created at a 10x multiple"},{value:"11.5% → 5.8%",label:"cloud spend as share of revenue"}],situation:"A mid-market fund was evaluating a patient engagement platform as the anchor for a buy-and-build strategy. Reported cloud spend was $4.5M — 10% of revenue, high for healthcare SaaS but not disqualifying. The stack looked modern and SOC 2 and HIPAA compliance were both claimed. Nothing in initial screening justified walking away, and the deal proceeded to LOI at 11x EBITDA.",approach:"Six weeks of technical diligence found actual spend was $5.2M — management had excluded several accounts — putting it at 11.5% of revenue. Development environments ran at production scale around the clock ($800K), databases were provisioned at 4x observed utilization ($600K), and no cost allocation or tagging existed anywhere. Total identified optimization was $1.8M, about 35% of spend. HIPAA implementation had real gaps: incomplete encryption at rest, audit logging holes, undocumented access reviews. Scored against the Technology Risk Assessment framework, the findings totaled 10 points and supported a −0.5x multiple adjustment.",outcome:"The firm repriced from 11x to 10.5x — a $5M reduction — and negotiated a $3M escrow covering security remediation. The 100-day plan targeted $1.5M of first-year optimization. Shutting down development environments outside business hours captured $400K in the first 30 days; right-sizing the worst-provisioned databases added $300K. By month twelve, spend had fallen from $5.2M to $3.6M while revenue grew 38%, and cloud dropped from 11.5% of revenue to 5.8% — below the industry benchmark. The $1.6M of EBITDA improvement translated to roughly $16M of enterprise value at the company's exit multiple."},{slug:"b2b-saas-technical-debt-walk-away",engagement:"Pre-Deal Assessment",sector:"B2B SaaS",scale:"$75M revenue",headline:"Strong growth and 95% net revenue retention masked a platform near end of life",metrics:[{value:"$3.9M",label:"remediation cost over 24 months"},{value:"18 months",label:"until the scalability ceiling"},{value:"No deal",label:"the firm walked away"}],situation:"On paper the target was excellent: 40% year-over-year growth, 95% net revenue retention, a defensible position, and discussions centered on 8x revenue. The investment thesis assumed the platform could carry that growth for the length of the hold period.",approach:"The core platform ran on a PHP framework that had reached end of life three years earlier. The original development team was gone, and the engineers who remained maintained the system through tribal knowledge and careful avoidance of change. There were no automated tests — each release took two weeks of manual testing. Fifteen years of unrefactored schema changes had produced tables with 200+ columns and queries measured in minutes. Modeling the compound interest on that debt was the core of the assessment: a framework migration that would have cost $200K five years earlier now required $2.5M and 18 months.",outcome:"The direct remediation cost was not what killed the deal. Engineers spent 60% of their time on workarounds, incident response, and manual testing — a 40-person team operating as though it were 16, roughly $3.6M in wasted annual labor, with feature velocity down 70% over three years. The platform could not scale past 50,000 concurrent users; the company was at 35,000 and adding 5,000 a quarter. Three remediation scenarios were modeled and all three failed within the hold period. The firm walked away. As the partner put it to the investment committee: the technical debt had consumed the value that made the deal attractive.",caveat:"The most useful diligence outcome is sometimes a deal that does not happen."},{slug:"portfolio-wide-optimization-program",engagement:"Portfolio Optimization",sector:"8 SaaS companies",scale:"$12M combined spend",headline:"Portfolio scale captured discounts no single company could negotiate alone",metrics:[{value:"$4.9M",label:"annual savings across the portfolio"},{value:"2.9 months",label:"payback on commitment aggregation"},{value:"15-20% → 35%",label:"negotiated discount rate"}],situation:"A mid-market fund held eight SaaS companies with combined cloud spend of $12M, ranging from $800K to $2.5M each. Every company optimized independently, and each was individually too small to negotiate meaningful commitment discounts — the portfolio was leaving money on the table simply by acting as eight buyers instead of one.",approach:"A four-phase program over 18 months, with $1.2M invested. Phase one aggregated commitments: a centralized team analyzed usage across all eight companies and negotiated a single enterprise discount program. Phase two deployed shared monitoring, security, and CI

1/CD platforms serving every company. Phase three standardized the estate — five companies on AWS, two on Azure, one on GCP — onto a single platform with common tooling. Phase four established the operating rhythm: monthly infrastructure calls, quarterly architecture reviews, and a cross-company rotation program.",outcome:"Aggregation moved the discount rate from 15-20% independently to 35% collectively, worth $2.1M annually and paying back in under three months — which funded everything that followed. Shared services cut $1M of duplicated tooling spend to $400K. Standardization reduced the infrastructure engineering requirement from 24 people to 14. The clearest lesson was about sequencing: lead with commitment aggregation because it is fast and self-funding, and standardize gradually, because forced migrations create resistance where voluntary adoption with incentives does not."},{slug:"fortune-500-carve-out-separation",engagement:"Carve-Out Separation",sector:"Corporate divestiture",scale:"$80M revenue",headline:"Eighteen months to separate a business unit fully entangled with its parent",metrics:[{value:"18 months",label:"to full operational independence"},{value:"$2.4M",label:"total separation cost"},{value:"100% / 80% / 60%",label:"shared auth / networking / databases"}],situation:"A PE firm acquired an $80M-revenue business unit from a Fortune 500 parent. The unit was not a standalone company in any operational sense: authentication ran entirely through the parent's tenant, four fifths of networking was shared, and customer data sat in a database cluster shared with the parent. The parent wanted out of the transition services agreement quickly; the buyer needed independent operations before it could do anything else.",approach:"Three months of pre-separation assessment mapped every shared dependency and scored the separation as high complexity — which set up the TSA negotiation. The agreement ran 18 months at $150K monthly with a 20% discount for early termination, aligning both sides on speed. Infrastructure replication followed over months three to nine: a dedicated identity tenant with 500 users migrated, replicated customer databases with independent backup and disaster recovery, independent networking, and dedicated monitoring. Data separation ran in parallel from month six, extracting 2M customer records from the shared cluster and migrating five years and 10TB of transaction history while the business kept operating.",outcome:"The unit reached full operational independence within the TSA window. The economics of a carve-out are dominated by two numbers that pull against each other — the cost of separation and the monthly cost of not having separated — and structuring the TSA with an early-termination discount put both parties on the same side of that tension. This is the shape of engagement where an interim CTO usually earns their keep: the work is a fixed-duration, full-time execution problem with a defined end state."}];function u(){return e.jsxs(e.Fragment,{children:[e.jsx(r,{title:"Representative Engagements",description:"Engagement patterns across diligence, portfolio optimization and carve-out separation — grounded in 127 portfolio companies and 73 PE interviews.",keywords:"cloud economics examples, technical due diligence example, PE portfolio optimization, carve-out separation, cloud waste EBITDA, private equity technology diligence, cloud spend benchmarks",path:"/engagements"}),e.jsx(l,{items:[{name:"Home",path:"/"},{name:"Engagements",path:"/engagements"}]}),e.jsx(d,{name:"Representative engagement patterns",description:"Engagement patterns across diligence, portfolio optimization, carve-out separation and value creation. Illustrative composites drawn from 127 portfolio companies and 73 private equity interviews, not accounts of individual clients.",items:s.map(a=>({name:`${a.engagement} — ${a.sector}, ${a.scale}`,description:a.headline}))}),e.jsx("section",{className:"page-header",children:e.jsxs("div",{className:"container",children:[e.jsx("div",{className:"page-header-label",children:"From the Research"}),e.jsx("h1",{children:"Representative Engagements"}),e.jsx("p",{children:"What this work looks like in practice, across diligence, optimization, separation, and value creation"})]})}),e.jsx("section",{className:"section",children:e.jsxs("div",{className:"container",children:[e.jsxs("aside",{className:"engagement-notice",children:[e.jsx("h2",{children:"Where these numbers come from"}),e.jsxs("p",{children:["Every figure on this page is drawn from the research behind"," ",e.jsx("a",{href:"https://cloudeconomicsbook.com",target:"_blank",rel:"noopener noreferrer",children:e.jsx("em",{children:"Cloud Economics for Private Equity"})}),": ",e.jsx("strong",{children:"127 portfolio companies analyzed between 2020 and 2025"}),", and structured interviews with"," ",e.jsx("strong",{children:"73 private equity professionals across 41 firms"}),". The numbers are medians and observed ranges, not best cases."]}),e.jsx("p",{children:"That also makes these composites rather than accounts of individual clients — which is precisely why the figures are defensible. A single engagement I picked because it went well would tell you less than the median of 127. Any resemblance to a specific company or transaction is coincidental."}),e.jsx("p",{children:"Read them for the shape of the problem and the order of magnitude rather than as a promise of outcome. Your infrastru

1cture is not the median."})]}),e.jsx("div",{className:"engagement-list",children:s.map(a=>{var n;return e.jsxs("article",{className:"engagement-card",children:[e.jsxs("div",{className:"engagement-meta",children:[e.jsx("span",{className:"engagement-type",children:a.engagement}),e.jsx("span",{className:"engagement-detail",children:a.sector}),e.jsx("span",{className:"engagement-detail",children:a.scale})]}),e.jsx("h2",{className:"engagement-headline",children:a.headline}),((n=a.metrics)==null?void 0:n.length)>0&&e.jsx("div",{className:"engagement-metrics",children:a.metrics.map((i,o)=>e.jsxs("div",{className:"engagement-metric",children:[e.jsx("div",{className:"engagement-metric-value",children:i.value}),e.jsx("div",{className:"engagement-metric-label",children:i.label})]},o))}),e.jsxs("div",{className:"engagement-body",children:[e.jsxs("div",{className:"engagement-block",children:[e.jsx("h3",{children:"Situation"}),e.jsx("p",{children:a.situation})]}),e.jsxs("div",{className:"engagement-block",children:[e.jsx("h3",{children:"Approach"}),e.jsx("p",{children:a.approach})]}),e.jsxs("div",{className:"engagement-block",children:[e.jsx("h3",{children:"Outcome"}),e.jsx("p",{children:a.outcome})]})]}),a.caveat&&e.jsx("p",{className:"engagement-caveat",children:a.caveat})]},a.slug)})})]})}),e.jsx("section",{className:"section section-alt",children:e.jsx("div",{className:"container",children:e.jsxs("div",{className:"engagement-footer",children:[e.jsx("h2",{children:"The frameworks behind these"}),e.jsx("p",{children:"The Technology Risk Assessment scoring, the due diligence question bank, and the integration complexity model used in these scenarios are all available as free downloads — the same tools used in live engagements."}),e.jsxs("div",{className:"engagement-footer-cta",children:[e.jsx(t,{to:"/resources",className:"btn btn-outline",children:"Get the Frameworks"}),e.jsx(t,{to:"/services",className:"btn btn-outline",children:"View Services"})]})]})})}),e.jsx("section",{className:"cta-section",children:e.jsxs("div",{className:"container",children:[e.jsx("h2",{children:"Recognize your situation here?"}),e.jsx("p",{children:"Book a free 15-minute discovery call. We'll discuss where you are and I'll tell you honestly whether I can help."}),e.jsx(t,{to:"/contact",className:"btn btn-primary",children:"Schedule a Call"})]})})]})}export{u as default};