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AI Quality of Earnings Adjustment Analysis Playbook

A target company is presenting $14.2M EBITDA. The buy-side quality of earnings report has identified $2.8M in potential adjustments. The seller's investment bank disputes $1.4M of those adjustments. The parties are in a negotiation standoff with a deal closing deadline in 21 days.

When to use this playbook

  • Use this playbook when the decision looks like the situation above: A target company is presenting $14.2M EBITDA.
  • It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Quality of Earnings Adjustment Analysis".
  • Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

  • Buy-side QoE report with all proposed adjustments and methodology
  • Sell-side response disputing $1.4M of adjustments
  • Supporting documentation for all disputed items
  • Letter of intent purchase price and EBITDA multiple
  • Comparable deal QoE adjustment rates in the sector

Attachments: Documents (Documents)

The Prompt

You are an M&A advisor mediating a QoE adjustment dispute between a buyer and seller. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. For each disputed adjustment, assess the merits: is the adjustment supported by GAAP, industry practice, or the specific deal context?
2. Identify the adjustments where the buyer has the strongest position and the adjustments where the seller's dispute is most credible.
3. Calculate the purchase price impact of each possible resolution: full buyer position, full seller position, and a negotiated middle.
4. Recommend the settlement position: which adjustments to concede, which to hold, and what compromise positions are defensible.
5. Tell me the deal structure alternatives (escrow, earnout, price reduction) that resolve the standoff without losing the deal.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

  • Per-adjustment merit assessment
  • Buyer vs. seller strength ranking
  • Purchase price impact by resolution scenario
  • Settlement position recommendation with concession/hold analysis
  • Deal structure alternatives for standoff resolution

Review before you act

  • Validate this output against source files before relying on it: For each disputed adjustment, assess the merits: is the adjustment supported by GAAP, industry practice, or the specific deal context?.
  • Validate this output against source files before relying on it: Identify the adjustments where the buyer has the strongest position and the adjustments where the seller's dispute is most credible.
  • Validate this output against source files before relying on it: Calculate the purchase price impact of each possible resolution: full buyer position, full seller position, and a negotiated middle.
  • Validate this output against source files before relying on it: Recommend the settlement position: which adjustments to concede, which to hold, and what compromise positions are defensible.
  • Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
  • Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
  • Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.

Why compare models on this

For Quality of Earnings Adjustment Analysis, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface per-adjustment merit assessment; buyer vs. seller strength ranking; purchase price impact by resolution scenario; settlement position recommendation with concession/hold analysis. Those are comparison artifacts — they only exist if more than one model runs. Models disagree on whether revenue is pull-forward, whether a contract is terminable, and how much working capital to normalize. Those fights are the diligence memo.

M&A Due DiligenceEarnings and Revenue QualityRecommendationHighDocuments

See governed multi-model AI on your own prompt

Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.