M&A Due Diligence

Conduct a comprehensive AI-augmented M&A due diligence covering financial, operational, legal, cultural, technology, and AI readiness with scored assessments...

1kalin@1kalin

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M&A Due Diligence — AI-Augmented Assessment

Run a structured due diligence analysis on any acquisition target. Covers financial, operational, technology, legal, cultural, and AI/automation readiness dimensions.

When to Use

Evaluating an acquisition target
Preparing sell-side due diligence materials
Assessing a merger partner
Running post-LOI deep dive
Investor or board due diligence request

How to Use

Tell your agent: "Run M&A due diligence on [Company Name]" or "Prepare due diligence for acquiring [target]."

The agent will walk through each dimension below and produce a scored assessment with red flags, deal-breakers, and a go/no-go recommendation.

Due Diligence Framework

1. Financial Health (Weight: 30%)

Revenue Quality Score (0-100)

Factor	Weight	Check
Revenue concentration	25%	Top client <15% of revenue = green, >30% = red
Recurring vs one-time	25%	>70% recurring = green, <40% = red
Revenue growth trend	20%	3-year CAGR >15% = green, declining = red
Gross margin	15%	>60% SaaS / >40% services = green
Cash conversion	15%	Operating cash flow / EBITDA >80% = green

Financial Red Flags

Revenue recognized before delivery
Related-party transactions >5% of revenue
Working capital deteriorating quarter-over-quarter
Customer acquisition cost (CAC) payback >18 months
Deferred revenue declining while bookings "grow"
Off-balance-sheet liabilities or operating leases hiding debt

Valuation Sanity Check

Metric	Healthy Range	Danger Zone
EV/Revenue	3-8x (SaaS), 1-3x (services)	>15x without hypergrowth
EV/EBITDA	10-20x	>30x
Price/FCF	15-25x	>40x or negative FCF
Net debt/EBITDA	<2x	>4x

2. Technology & Product (Weight: 20%)

Tech Stack Assessment

Architecture: Monolith vs microservices vs serverless
Technical debt score: deployment frequency, change failure rate, MTTR
Security posture: SOC 2, ISO 27001, penetration test recency
Scalability: Can 10x current load without rewrite?
AI/Agent readiness: API-first? Automation potential?
IP ownership: Clean IP assignments from all contributors?

Product-Market Fit Signals

Net Revenue Retention >110% = strong PMF
Logo churn <5% annually = healthy
NPS >40 = good, >60 = excellent
Organic growth % of total = true demand signal

3. Legal & Compliance (Weight: 15%)

Legal Checklist

Pending or threatened litigation
IP infringement claims or risk
Data privacy compliance (GDPR, CCPA, HIPAA)
Employment law compliance (contractor misclassification)
Environmental liabilities
Tax compliance and audit history
Change-of-control clauses in key contracts
Non-compete/non-solicit enforceability

Regulatory Risk Score

Industry-specific regulations mapped
Compliance cost as % of revenue
Pending regulatory changes that could impact operations

4. Operational Excellence (Weight: 15%)

Operations Scorecard

Area	Green	Yellow	Red
Employee turnover	<15%	15-25%	>25%
Key person dependency	Distributed	Some concentration	Single point of failure
Process documentation	Comprehensive	Partial	Tribal knowledge
Vendor concentration	Diversified	2-3 critical	Single vendor lock-in
Customer support quality	<2hr response, >90% CSAT	4-8hr, >80% CSAT	>24hr, <70% CSAT

5. Cultural & People (Weight: 10%)

Integration Risk Assessment

Leadership alignment on vision and values
Compensation structure compatibility
Remote/hybrid/office culture match
Decision-making style (flat vs hierarchical)
Glassdoor/Blind sentiment analysis
Key employee retention risk (flight risk score)

Retention Package Benchmarks

Role Level	Retention Period	Typical Package
C-Suite	12-24 months	1-2x base + equity acceleration
VP/Director	12-18 months	0.5-1x base + equity
Key IC	6-12 months	Spot bonus + equity refresh

6. AI & Automation Readiness (Weight: 10%)

Agent-Era Value Score

Data quality and accessibility (structured, labeled, API-accessible?)
Process automation potential (which workflows can agents run?)
AI talent on team (ML engineers, data scientists, prompt engineers)
Existing AI/ML models or IP
Competitive moat from proprietary data or models

Post-Acquisition AI Uplift Estimate

Department	Automation Potential	Annual Savings
Customer Support	40-60% ticket deflection	$120K-$400K
Sales Operations	30-50% pipeline automation	$80K-$200K
Finance/Accounting	50-70% reconciliation	$60K-$150K
HR/Recruiting	40-60% screening	$50K-$120K
Legal/Compliance	30-40% contract review	$40K-$100K

Scoring & Output

Overall Deal Score (0-100)

80-100: Strong buy — proceed to LOI/definitive agreement
60-79: Conditional — address specific risks before proceeding
40-59: Caution — significant risk areas need resolution
0-39: Walk away — deal-breakers present

Deal-Breaker Triggers (auto-fail regardless of score)

Undisclosed material litigation
Revenue fraud or restatement risk
Key customer representing >40% revenue with no contract
Unresolvable IP ownership dispute
Regulatory action that could shut down operations
Key founder/employee departure with no retention agreement

Output Template

text

# M&A Due Diligence Report: [Target Company]
Date: [YYYY-MM-DD]
Prepared for: [Acquirer]

## Executive Summary
- Overall Score: [X/100]
- Recommendation: [BUY / CONDITIONAL / WALK]
- Key Strengths: [3 bullets]
- Critical Risks: [3 bullets]
- Estimated Integration Cost: $[X]
- Post-Acquisition AI Uplift: $[X]/year

## Detailed Scores
| Dimension | Score | Weight | Weighted |
|-----------|-------|--------|----------|
| Financial Health | /100 | 30% | |
| Technology & Product | /100 | 20% | |
| Legal & Compliance | /100 | 15% | |
| Operational Excellence | /100 | 15% | |
| Cultural & People | /100 | 10% | |
| AI & Automation Readiness | /100 | 10% | |
| **Overall** | | | **/100** |

## Red Flags
[Numbered list with severity and mitigation]

## Integration Roadmap
- Day 1-30: [Quick wins]
- Day 31-90: [System integration]
- Day 91-180: [Full operational merge]
- Day 181-365: [Optimization and AI deployment]

## Recommended Next Steps
[3-5 specific actions]

2026 Benchmarks

M&A Market Context

Average SaaS acquisition multiple: 6-10x ARR (down from 15-20x in 2021)
Due diligence period: 60-90 days (accelerating with AI tools)
Integration failure rate: 70-90% of M&As fail to deliver expected value
#1 reason for failure: Cultural mismatch and poor integration planning
AI-ready targets command 15-25% premium over comparable non-AI companies

What's Changed in 2026

AI/agent readiness is now a core valuation driver
Data assets valued separately (proprietary training data = moat)
Per-seat SaaS revenue declining — usage-based models preferred
Remote-first companies easier to integrate (no office consolidation)
Regulatory complexity increasing (EU AI Act, state privacy laws)

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