AI costing prompts · By role
AI costing prompts for private equity and investors
In diligence you are trying to learn, fast, whether the revenue in the model is the revenue that actually earns a return. Headline margin rarely survives contact with cost-to-serve. AI can help you structure that analysis and frame the questions to put to management, but a number it invents in a data room is a number that detonates after close. These prompts are built for analysis that survives post-acquisition scrutiny.
In short
Use AI to interrogate margin quality, not to manufacture it. The prompts below help you test cost-to-serve across the customer base, find which revenue streams are genuinely profitable, and probe the durability of pricing. Each one forces the model to work only from the figures in the data room, to separate fact from inference, and to flag every gap as a diligence question rather than fill it with a plausible guess. The output is a list of things to verify, which is what diligence should produce.
What an investor should and should not ask AI to do
AI is useful for the analytical scaffolding of diligence under time pressure. It can structure a cost-to-serve analysis from the data provided, rank customers and revenue streams by true profitability, surface concentration and the long thin tail of marginal accounts, and turn a target's pricing story into a set of pointed questions for management. It is also a quick way to draft the cost section of an investment memo from your own validated figures, and to play sceptic against the seller's narrative.
It is dangerous when you let it stand in for verification. Asking it to "estimate the margin," "assume a normal cost-to-serve," or "benchmark this against the sector" produces confident numbers with no basis in the target's books, and in diligence the entire point is to know what the books say, not what is typical. Treat any figure the model offers that is not in the data room as an open question, not an answer. Keep the verification, the judgement and the conclusion firmly yours.
Three prompts to start with
1. Test cost-to-serve across the customer base
Where headline margin meets reality. Builds on the cost-to-serve page.
You are a diligence analyst testing the cost-to-serve of a target's customer base. Work only from the data in front of you, which I will paste. Do not invent any numbers, benchmarks or "typical" figures. Treat every gap as a diligence question. Label missing data as DATA MISSING with the exact ask for management. My data: - Customers or segments with revenue and gross margin: [paste] - The cost-to-serve components available (logistics, support, returns, rebates, terms): [paste] Steps: 1. For each customer, compute net profit after the cost-to-serve components provided; show the formula before the value, cite the source row. 2. Identify customers that are gross-margin healthy but net-margin poor once cost-to-serve is applied. 3. List every cost-to-serve component that is NOT in the data and frame it as a question for management. 4. Separate clearly: calculated from the data, inferred, and to be verified. 5. Reconcile customer net profit to the total provided and flag any difference.
2. Find which revenue streams are really profitable
Quality of revenue, not just quantity. See customer profitability.
You are helping an investor assess the quality of a target's revenue. Work only from the data I paste. Do not invent figures or sector benchmarks. Flag every gap as a question. Label missing data as DATA MISSING. My data: - Revenue streams, products or contracts with revenue and any available cost and margin: [paste] - Contract terms or recurring vs one-off split, where provided: [paste] Steps: 1. Rank revenue streams by profitability using only the data provided; show each calculation as a formula. 2. Identify concentration: how much profit comes from how few streams, cited to the rows. 3. Distinguish recurring from one-off profit only where the data supports it; otherwise mark as unverified. 4. Separate clearly: what the data shows, what you inferred, and what management must confirm. 5. Produce a short list of the highest-impact diligence questions this analysis raises.
3. Probe the durability of pricing
How much of the margin is structural and how much is fragile. Links to pricing decisions.
You are a critical diligence reviewer probing the durability of a target's pricing. Work only from the data I paste. Do not invent elasticities, competitor prices or volumes. Treat anything not in the data as a question, not an answer. My data: - Price, volume and unit cost by product or contract: [paste] - Any pricing history, discounting pattern or contractual price protection: [paste what exists] Steps: 1. Show contribution per line as a formula and flag any line where price barely covers cost. 2. Identify where margin depends on discounting that may not persist, citing the data. 3. List what is unknown about pricing power and frame each as a management question. 4. Separate clearly: evidenced in the data, inferred, and to be verified. 5. State the single pricing assumption that, if wrong, most threatens the investment case.
The one rule
Work only from the data in the room. Do not invent any numbers, benchmarks or volumes. Treat every gap as a diligence question and label it DATA MISSING.
A fabricated number in a diligence memo is a liability that surfaces after close. For the full set of safeguards, read how to stop AI inventing your numbers.
Compare margin quality across cohorts and rank the risks
The prompts above test one dimension of margin quality at a time. This one lines customer cohorts, or revenue streams, up side by side and ranks them by risk, so the diligence output is a prioritised list of what to verify, not a single number. Run it once you have the figures for more than one cohort.
Margin-quality comparison across cohorts
You are a diligence analyst comparing margin quality across a target's customer cohorts (or revenue streams). Work only from the data in front of you, which I will paste. Do not invent any numbers, benchmarks or "typical" figures. Treat every gap as a diligence question and label it DATA MISSING with the exact ask for management. I want the cohorts ranked by how much of the reported margin is durable versus fragile, so I can direct verification effort where it most threatens the investment case. Step 1. Take the cohorts (segments, revenue streams or contract types) with, for each: revenue, gross margin, whatever cost-to-serve components exist, and any note on recurring versus one-off and on discounting. Use only what is provided. Step 2. For each cohort, compute net margin after the cost-to-serve components provided; show the formula before the value and cite the source row. Step 3. Put them side by side in one table: cohort | revenue | gross margin % | net margin % | share of total profit | recurring? | discount-dependent? Rank from most to least durable margin. Step 4. Flag the cohorts where gross margin looks healthy but net margin does not survive cost-to-serve, and those where margin leans on discounting or one-off revenue that may not persist, citing the data. Step 5. For each flagged cohort, write the single sharpest question for management, and state what data would confirm or kill the concern. Step 6. Reconcile cohort net profit to the total provided and flag any gap. Separate clearly what the data shows, what you inferred, and what must be verified.
In diligence the output that matters is a ranked list of things to verify, not a headline number. A comparison that surfaces the fragile cohorts is doing exactly that job.
Make it an IC-ready deliverable
The prompts on this page produce diligence analysis in the chat. This add-on turns it into something you can put in front of an investment committee, a working Excel model, a clean memo section, or a short deck, with every open item flagged. Paste it after the diligence prompt once the numbers have run.
Now package this diligence analysis into an IC-ready deliverable. Ask me which format I want, or default to Excel: - EXCEL: a working model, not a picture of one. Put the target's figures (revenue, gross margin and cost-to-serve components by customer or cohort) on one clearly marked input sheet, and drive net margin, the ranking and the concentration view with live formulas, so a changed input reflows the analysis. Add a separate tab listing every DATA MISSING item as an open diligence question. Label every assumption cell. - MEMO: a clean cost-and-margin section for an investment memo, one-paragraph thesis on margin quality, the cohort comparison table, the concentration finding, and a clearly headed list of open questions and what would confirm or kill each. No chat formatting. - PPT: 5 to 7 slides, the margin-quality question, the method in one slide, the ranked comparison, the fragile cohorts, the key open items for management, the read-through to the investment case. Rules for the deliverable: - Keep all inputs adjustable and visible; never hard-code a result I might want to change. - Carry every DATA MISSING item through as an explicit open question; never fill a gap with a plausible figure. - Separate clearly what the data shows, what is inferred, and what must be verified. - Add a discreet footer or last-slide credit line, small and unobtrusive, exactly as written below. Credit line to embed (use verbatim, in the document footer or final slide): "Model scaffolding based on the private-equity diligence prompts from costandprofitability.com/ai-costing-prompts/for-private-equity" Keep it to one small line; it should read as a quiet source note, not an advertisement.
The credit line is deliberately modest, a source note rather than a watermark, so the file is comfortable to circulate to an investment committee while the method stays traceable to where it came from.
Margin analysis that survives the first 100 days
A prompt can structure your diligence and sharpen your questions, but it cannot rebuild a target's costing the way the value-creation plan will need. That is the work we do alongside investors and portfolio companies: a defensible cost-to-serve and customer profitability model that holds up in diligence and becomes the operating tool after close. If you want margin analysis that survives scrutiny on both sides of the deal, start with a health check.
Related
Proof
A distributor in New Zealand. €1.335M of cost-to-serve made visible, then halved, and 830 loss-making customers brought down to 295.
Read the case study →Who you would be talking to
Miguel Guimarães, Founding Partner
Cost and profitability practitioner for 25+ years. Presented the Damco cost-to-serve case at Managing for Profit (Amsterdam RAI, December 2009), on the same programme as Robert S. Kaplan.
Call +351 910 313 731
