Confidential Information Memorandum AI: A Business Broker's Workflow

Use AI to draft the structure, turn normalised financials into plain narrative, and tighten the prose. Then check every figure and claim against a source document before a buyer sees it. AI writes a confident first draft quickly; a broker's job is to make that draft true, and to keep the seller's identity out of the prompt.

A confidential information memorandum (CIM) is the document that sells a business to a qualified buyer after they sign an NDA. It often runs to dozens of pages. Drafting one by hand takes days because the facts live in several places: owner interviews, management accounts, contracts, staff lists. AI cuts the writing time, not the gathering time, and it does nothing at all for accuracy. So the order matters: gather and number the facts first, draft second, check last.

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What a CIM must contain, and what AI can draft from it

Buyers expect a recognisable shape, so start from an outline and let AI fill sections, not invent them. A reasonable outline for a small or mid-sized owner-run company:

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1. Executive summary: one page, the business, why now, headline numbers.
2. Business description: what is sold, to whom, how the work is delivered.
3. Customers and revenue concentration: segments, retention, the largest accounts as a percentage, never named until later.
4. Operations and people: team structure, owner dependence, key roles.
5. Market and competition: the sector, the position, the real competitors.
6. Financial performance: normalised profit and loss, margins, working capital.
7. Growth opportunities: what a buyer could do that the owner has not.
8. Transaction overview: what is for sale, the process and timing.

AI is good at sections 1, 2, 5 and 7 when you give it facts, and at turning tables into sentences for section 6. It is weakest on customers and operations, because those depend on things nobody has written down, and it will fill the gap with plausible filler. Keep that in mind when you decide what to hand over.

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What the Flippa BrokerAI CIM Builder is, and what it is not

Flippa published a launch post on 13 April 2026 describing a CIM Builder inside its BrokerAI layer. Per that post, it starts from an active listing on the platform, combines the owner's inputs, the broker's expertise and Flippa's data on comparable transactions, and produces a formatted memorandum. Flippa says Gamma handles the document rendering, and lists the sections every output includes: an executive summary, a financial analysis benchmarked against its comparable sales, "AI-identified" growth opportunities, and market and competitive context. The same post names two other BrokerAI tools, an AI Listing Generator and an AI Valuation Consultant, as live, and marks AI buyer qualification as coming soon.

Three cautions. The claims about speed ("five minutes instead of five weeks") and the size of the comparable-deal dataset are Flippa's own marketing and I have not been able to test them. The tool is described for online and digital businesses listed on Flippa with one of its brokers (the post adds that Flippa is bringing BrokerAI tools to its self-serve marketplace too), so it does not obviously fit a roofing firm or a brokerage sold privately. And a polished layout is not a verified document: the checking steps below apply to its output exactly as they apply to a chatbot's, and a growth section written by a model is the part to check hardest.

If you sell digital businesses on that platform, try it on one listing and run the fact-check pass on what it gives you. If you sell other kinds of company, the rest of this tutorial uses a general assistant such as ChatGPT, Claude or Gemini, on a business plan, with your own outline. The workflow is the same either way.

The seller file you assemble before you open an AI tool

Garbage in, garbage out has a particular shape here: if you paste a half-complete interview and two years of accounts, the model will smooth over every hole. Build a single source pack first, and number its documents so the fact-check can cite them.

• S1: normalised profit and loss for three years plus year to date, with the add-back schedule.
• S2: balance sheet summary and working capital notes.
• S3: customer list with revenue by customer, kept in your files and never pasted whole.
• S4: staff list with roles, tenure and pay bands, anonymised.
• S5: contracts and leases summary: terms, renewals, change-of-control clauses.
• S6: the owner interview notes, written up in the owner's words.
• S7: public facts: sector data, competitor information, with where it came from.

The numbering is the whole trick. Every sentence in the final CIM should trace back to an S-number, and a sentence that traces to nothing is either a claim to verify or something the model made up. If you want help getting the owner side of this ready, the tutorial on getting records ready to sell is written for owners and can be sent to them.

Keeping the seller anonymous in the prompt

A CIM is confidential because it names the business. A teaser goes out without a name (the tutorial on writing an anonymous teaser with AI covers that first document); the CIM goes only to buyers who have signed. So treat your prompt the same way: an AI tool is a third party, and your seller agreement and NDA set what you may share with it. Check them, and check the vendor's current data terms for the plan you use. Business plans generally do not train on your content by default, but that is a vendor setting you should confirm yourself, not assume.

A practical set of rules that stays within most agreements:

• Replace the business name with "the Company" and customers with "Customer A, B, C" before pasting.
• Give percentages and bands for customer revenue, not names.
• Round locations to a type ("a regional centre") and leave named places out.
• Keep the unredacted pack in your own storage, and re-insert names only in the final document.
• Tell the seller in your engagement terms that AI tools are used for drafting, and what is shared.

If you operate under a regulator or a professional body, ask whether it has rules on client data in AI tools; they differ too much to list here.

A worked example, with numbers: a roofing company

All figures here are illustrative. Say you are selling a roofing company with 14 staff. Revenue last year was $2.4m, the owner's reported profit was $310,000, and the normalised profit after add-backs was $395,000. Your S1 pack shows the add-backs: $48,000 of owner's family salary above market, $22,000 of one-off legal fees, and $15,000 of vehicle costs that were personal. That gives $310,000 + $85,000 = $395,000. Margin on revenue is about 16.5 percent.

The prompt for the financial section, after redaction:

You are helping draft the Financial Performance section of a
confidential information memorandum. Use ONLY the data below.
If something is not in the data, write [NOT PROVIDED] rather than
estimating.

Data (illustrative):
- Revenue FY1 $2.1m, FY2 $2.25m, FY3 $2.4m
- Reported profit FY3 $310,000
- Add-backs FY3: family salary above market $48,000; one-off legal
  $22,000; personal vehicle costs $15,000
- Normalised profit FY3 $395,000
- Gross margin FY3 38 percent
- Top customer 14 percent of revenue; top five 41 percent

Write 250 words for a buyer: plain English, no adjectives such as
"strong" or "impressive", each figure stated once, with a one-line
caution on what the add-backs rely on.

A likely output (illustrative): "Revenue grew from $2.1m to $2.4m over three years, an increase of about 14 percent. In the latest year, reported profit was $310,000. Three add-backs total $85,000, giving normalised profit of $395,000, a margin of 16.5 percent. The add-backs depend on the buyer accepting that the family salary and vehicle costs would not continue under new ownership." Check it: 2.4 divided by 2.1 is 1.143, so 14 percent is right. 395 divided by 2,400 is 16.46 percent, so 16.5 is right. Good.

Now the realistic failure. Run it again with the instruction about adjectives removed and the model may write "revenue growth of 14 percent compound" or "consistent margin expansion". Neither is supported: FY1 to FY3 is two steps, and the data gives no earlier margin. This is where AI drafts go wrong in financial sections, not in arithmetic, but in the confident phrase that sounds like analysis. The fix is the "use only the data" instruction, the [NOT PROVIDED] rule, and the check below.

Turning financials into narrative without inventing a story

Narrative in a CIM explains movement: why revenue rose, why margin dipped, what is one-off. The owner knows; the accounts do not. The wrong order is to ask the model "why did margin fall?". It will produce reasons. The right order is to ask the owner, put their answer in S6, then ask the model to write it up.

Take a three-line interview note: "Margin dipped in year two because of a roofing material price rise we passed on late; we changed our quote terms in year three." The prompt is: "Using only this note and the figures given, write three sentences explaining the year-two margin dip. Do not add causes." The output stays within what the owner said. Compare that with the lazy prompt, which might add "supply chain disruption", something nobody told you.

For monthly or management-pack data you may want to see how ChatGPT handles an accounts export into a management pack, since the same structure feeds the CIM's financial section.

Prompt patterns that work for the other sections

Executive summary. Write this last, from the finished sections. "Summarise the sections below into a one-page executive summary. Quote numbers only as they appear. Do not add any fact that is not in the text." Reading a summary that cannot introduce new facts is easy to check.

Business description. Feed it S6 and ask for plain description. "Describe what the Company does and for whom in 200 words, using the owner's own terms. List any assumptions you made at the end." The assumptions list is a gift: it is the model telling you where it guessed.

Growth opportunities. This is where brokers are tempted to let AI be creative. Be careful: a buyer reads it as the seller's claim. Give it opportunities the owner has stated or that follow directly from the data, such as "the Company turns away work in the busy season because of crew capacity", and ask it to word them without projections. No revenue forecasts from a model.

Market section. Use only public facts you have collected in S7 with their sources. If you ask a chatbot for market size, you will get a number with no source. Leave it out or find a primary source.

The fact-check pass, line by line

This is the step that decides whether AI is a help or a liability, so give it a procedure and a time slot.

1. Number every claim. Paste the draft into a document and highlight every sentence containing a number, a date, a name, a superlative or a comparison.
2. Assign a source. Next to each, write the S-number. If there is no S-number, mark it red.
3. Recompute every figure. Percentages, growth rates, margins. Use a spreadsheet, not the model.
4. Strip superlatives. "Leading", "unique", "best-in-class" need evidence or go.
5. Owner read. The owner reads the whole draft and initials each page. Owners catch what only they know, such as a customer who left last month.
6. Second broker read. A colleague reads for tone and for anything a buyer's adviser would challenge.

A realistic miss: a draft states "the Company has served its largest customer for over ten years". Source S5 shows the contract began eight years ago, but the owner's interview note says "ten years" because the relationship pre-dated the contract. The sentence is arguably true and arguably not. The fix is to write what the document supports: "has traded with its largest customer for about ten years, under a contract since year eight". Buyers' advisers find these. Better that you do.

Budget honestly. For a 25-page CIM, I would plan two hours of AI-assisted drafting after the source pack exists, and three to four hours for the check. That is a long way short of five weeks, and it is a time you can defend.

Three smaller examples of prompts that save time

A tone pass. Paste one section and ask: "Tighten this to 20 percent fewer words, keep every figure and name exactly, and flag any sentence you changed in meaning." You then compare the flagged sentences only.

A buyer-question list. After the draft is done: "You are a buyer's adviser reading this CIM. List 15 questions you would ask the seller, ranked by how likely they are to affect price." This is a good use of a model, since a list of questions is not a claim. Take the top ten to the owner and prepare answers; it also tells you which sections are thin.

A consistency check. "List every number in this document and where it appears. Flag any number that appears twice with different values." Models are decent at this, and it catches a headcount of 14 in one section and 13 in another. Still verify the flagged ones yourself, and do not trust a clean result as proof there are no inconsistencies.

Before and after: one paragraph, edited properly

Here is the sort of first draft a model gives for the operations section when the prompt is loose (illustrative): "The Company boasts a highly skilled and loyal workforce, with an experienced management team that ensures smooth execution of every project." Nothing in it can be checked, and a buyer's adviser will skip it.

After you apply S4 and S6, it reads: "The Company employs 14 people: eight roofers, three labourers, two estimators and an office administrator. The longest-serving roofer has been with the Company for eleven years; the median tenure is four. The owner prices every job over $20,000 personally and attends most site handovers." The second version is longer, plainer and far more useful, because every clause traces to a source. It also tells the buyer that the owner is a dependency, which is honest and which you would rather say first than have discovered. This kind of edit, replacing a quality claim with a countable fact, is the single most valuable thing you do to an AI draft.

A short example for a different business: an insurance broker

Suppose the seller is a small insurance brokerage. The financial section looks different: revenue is commission and fees, and the key figures are renewal retention, the book's size and the split between commission and fee income. Say the source pack shows retention of 91 percent on 2,300 policies, with 78 percent of income from commission. The prompt keeps the same shape: "Use only these data points, write [NOT PROVIDED] for anything missing". The check differs: retention must be defined in the CIM (by policy count or by value?), and the source pack must say which. A model will happily write "91 percent retention" without a definition, and a buyer will ask which one. Put the definition in the draft, not in the appendix.

Confidentiality at the finish line

When the draft is final, re-insert real names only in a clean copy, in your own system, not in the AI tool. Delete the redacted conversations if your plan lets you, and note where the working drafts are stored. Mark the document with the buyer's name or an ID if your CRM supports watermarking, so a leaked copy can be traced. Send it through a data room or a controlled link rather than by attachment.

Keep a one-page record for each listing: which tools were used, which sections AI drafted, who checked them and when. If a buyer's adviser asks how the document was produced, you have an honest answer, and if a dispute comes later you have evidence of a careful process. Your engagement letter with the seller should say that AI tools may be used for drafting under your supervision; the engagement letter tutorial has wording for that kind of clause, and a solicitor should review yours.

How to tell the workflow is paying off

Track three numbers across your next five listings: hours from source pack complete to CIM approved by the owner; the number of factual corrections the owner or a buyer's adviser makes after your own check; and the number of buyer questions in the first week that the CIM should have answered. If hours fall and corrections stay flat or drop, the workflow is working. If hours fall and corrections rise, you are skipping the check, and the saving is borrowed from your reputation.

Once the CIM is out, the buyer's side starts. The due diligence checklist tutorial covers what they will ask for next, and a good CIM makes that list shorter.

Questions brokers ask before using AI on a CIM

Can I put the seller's financials into ChatGPT or Claude?

Only if your agreement with the seller and your NDA allow it, and only on a plan with business data protections. Strip the seller's name and customer names first, and keep the identifying data in your own files. Check the vendor's current data terms before the first upload.

Does Flippa's CIM Builder work for non-online businesses?

Flippa describes it as built for digital business sales, launched from an active Flippa listing with a broker. For a roofing company or an insurance broker you would use a general assistant with your own outline. Check Flippa's current pages for scope.

How long should the fact-check pass take?

Plan for at least as long as the drafting took, and usually longer. Every number, claim and name in the draft gets ticked against a source document. An hour of drafting with AI often needs two hours of checking, which is still faster than a blank page.

Who is liable for a wrong figure in a CIM?

That depends on your agreements and the law where you operate, and I cannot advise on it. Most CIMs carry a disclaimer that buyers must do their own diligence, but a solicitor should check yours. Accuracy is still your reputation.

Further reads

• Selling an Accounting Practice: Prepare the Due Diligence Pack With AI — A professional-practice version of the same documents.
• AI Readiness Checklist for Accountants, Solicitors, Consultants — Set data rules before any seller file reaches an AI tool.
• Do AI Business Valuation Tools Work? What They Miss for Brokers — What AI valuation tools miss before the CIM stage.
• AI for Chartered Accountants: What to Tell Clients About Your AI Use — How to tell clients about your AI use.
• Dental Practice Valuation Explained: Prepare Your Figures With AI — How an owner prepares figures before a practice sale.
• Business Broker CRM With AI: What to Compare Before You Buy — A buying guide for brokerages: buyer and listing pipelines, NDA gating, which AI features matter, and what four seats cost on each CRM type.
• Business Broker Marketing: AI Buyer Outreach and Anonymous Listings — A blind fact sheet, a leak list and four outreach steps that let a broker use AI for buyer emails and portal copy without exposing the seller.
• Business Broker NDA Process: Qualify Buyers With AI Screening — A four-gate buyer process for brokers: screening questionnaire, NDA step, proof-of-funds checklist and a scorecard, with AI drafting and people deciding.
• Seller Discretionary Earnings With AI: Recast and Find Add-Backs — Follow a broker's recast from reported profit to supported seller discretionary earnings, with a reviewable add-back register.
• AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.

Sources: Flippa's post of 13 April 2026 launching the BrokerAI CIM Builder (read 4 November 2026); Flippa's own descriptions are marketing claims and are reported here as such.