{"ok":true,"engine":"aicost.portfolio-ebitda-lift","inputs":{"annualRevenueUsd":40000000,"ebitdaMarginPct":15,"annualAiSpendUsd":3000000,"annualCloudSpendUsd":3000000,"aiSavingsPct":30,"cloudSavingsPct":20,"exitMultiple":12,"implementationCostUsd":250000,"portfolioCompanies":1,"adoptionRatePct":100},"result":{"ok":true,"verdict":"MATERIAL LIFT","verdictReason":"375 bps sits inside the 200-400 bps range PE operating teams target from AI and cloud cost work.","addressableSpendUsd":6000000,"aiSavingsUsd":900000,"cloudSavingsUsd":600000,"totalSavingsUsd":1500000,"currentEbitdaUsd":6000000,"newEbitdaUsd":7500000,"currentMarginPct":15,"newMarginPct":18.8,"marginExpansionBps":375,"ebitdaUpliftPct":25,"benchmark":{"exceptionalBps":400,"materialBps":200,"moderateBps":100,"note":"PE value-creation teams target roughly 200-400 bps from AI and cloud cost optimization."},"exitMultiple":12,"evLiftUsd":18000000,"evLiftPct":25,"implementationCostUsd":250000,"paybackMonths":2,"firstYearNetUsd":1250000,"portfolio":null,"monthlyUsd":125000,"cost":1500000,"memo":"On $40,000,000 of revenue at a 15% EBITDA margin, the addressable AI and cloud cost base is $6,000,000. Applying 30% AI and 20% cloud savings releases $1,500,000 a year, lifting EBITDA from $6,000,000 to $7,500,000, an expansion of 375 bps to a 18.8% margin (material lift). At a 12x exit multiple that is worth about $18,000,000 of enterprise value, roughly 25% of today's implied EV. Implementation pays back in about 2 months, with $1,250,000 net in year one. The savings rates are the assumption to test: model the actual workloads before underwriting them.","questions":["What share of the AI spend runs on the most expensive model tier, and could a routing policy move it without hurting quality?","What is the current cache-hit rate, and what would a serious caching layer do to cost per request?","Is any cloud capacity committed or reserved, and what is the utilization against it?","Who owns cost in the org today, and is there a budget or guardrail enforced in the gateway?","The model assumes 30% AI and 20% cloud savings. What evidence supports those rates for this specific workload mix?","Does the plan survive if the company's AI usage doubles during the hold period?"],"assumptions":["Cost savings are treated as flowing to EBITDA close to 1:1, which holds for opex-classified AI and cloud spend.","Savings rates of 30% (AI) and 20% (cloud) are analyst inputs, not measured results. *","Exit multiple of 12x is the fund's assumption. *","Excludes revenue-side effects, headcount changes, and any capitalized spend."],"sensitivities":[{"driver":"aiSavingsPct","note":"Model routing is usually the largest single lever; the price spread between tiers can exceed 5x."},{"driver":"exitMultiple","note":"Value creation scales linearly with the multiple, so the EV figure is only as good as that assumption."},{"driver":"adoptionRatePct","note":"Portfolio roll-ups fail on adoption, not on math. Haircut it honestly."}],"_model":"portfolio-ebitda-lift"}}