Advanced Business Tech #TDD-report #RAG-rating #due-diligence #M&A

Due Diligence Report Language

5 exercises — complete the technical due diligence language module: TDD report structure and audience calibration (executive summary vs technical findings vs risk register), RAG status written communication patterns (condition precedent, material liability, manageable risk), risk register field vocabulary (risk ID, likelihood × impact, residual risk, owner), formal recommendation language and professional liability hedging, and verbal due diligence delivery (technical red flag communication, handling CTO pushback, professional neutrality).

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TDD report language quick reference
  • Report structure: Executive summary (VC/board, business language, $) → Technical findings (CTO, technical terms) → Risk register (integration team, tabular, actionable) → Remediation roadmap (phased plan).
  • RAG writing: Red = no hedging ("condition precedent / we recommend against"). Amber = risk + path ("manageable / recommend allocating $X"). Green = concise and affirmative ("no action required / within expected parameters").
  • Risk register fields: ID, domain, description, RAG, likelihood, impact, score, current mitigation, residual risk, recommended action, owner, target date. Owner + date = actionability.
  • Recommendation register: "We recommend" (advisory) → "We strongly recommend" (elevated) → "Conditional on remediation of" (near-blocking) → "Condition precedent" (Red/blocking).
  • Verbal pushback: Acknowledge + invite evidence + hold position. "Our current rating stands based on evidence available during the assessment window."
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The TDD lead is structuring the final report. She explains to a junior assessor: "The biggest mistake in TDD reporting is writing everything in one voice for one audience. The VC partner reading the executive summary does not want to read the same document as the CTO reviewing the technical findings. And the deal team working with the risk register needs something different from both."

How is a TDD report structured, and how does audience calibration affect how findings are written?