AI Revenue Quality Assessment Playbook

A PE firm is evaluating a $48M EBITDA B2B SaaS business. Management is presenting $92M ARR with 94% gross retention and 112% net retention. The deal is priced at 18x EBITDA. Due diligence has 30 days remaining.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A PE firm is evaluating a $48M EBITDA B2B SaaS business.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Revenue Quality Assessment".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Customer contract file (all contracts >$50K annually)
• MRR/ARR bridge (24 months)
• Churn and expansion data by cohort
• Revenue recognition policy and deferred revenue schedule
• Management's ARR definition and calculation methodology

Attachments: Documents (Documents)

You are a PE deal associate conducting revenue quality due diligence on a $48M EBITDA SaaS business priced at 18x. I am attaching:

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

Produce:
1. Validate management's ARR figure: identify any contracts counted as ARR that are non-recurring, usage-based without minimums, or at risk of non-renewal.
2. Assess the 94% gross retention claim: calculate retention independently from the MRR bridge and identify any cohorts with material deviation.
3. Identify revenue concentration risk: what percentage of ARR is from the top 10 customers, and what is the customer cliff (largest customer as % of ARR)?
4. Assess whether any revenue was pulled forward (multi-year prepayments, implementation fees counted as ARR) that would reduce normalized ARR.
5. Tell me the adjusted ARR, adjusted EBITDA multiple at adjusted ARR, and the three biggest revenue quality risks I should take to the IC.

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

What to expect

• Validated ARR with adjustments
• Independent gross retention calculation
• Revenue concentration and customer cliff analysis
• Pull-forward revenue identification
• Adjusted metrics and top 3 IC risks

Review before you act

• Validate this output against source files before relying on it: Validate management's ARR figure: identify any contracts counted as ARR that are non-recurring, usage-based without minimums, or at risk of non-renewal.
• Validate this output against source files before relying on it: Assess the 94% gross retention claim: calculate retention independently from the MRR bridge and identify any cohorts with material deviation.
• Validate this output against source files before relying on it: Identify revenue concentration risk: what percentage of ARR is from the top 10 customers, and what is the customer cliff (largest customer as % of ARR)?.
• Validate this output against source files before relying on it: Assess whether any revenue was pulled forward (multi-year prepayments, implementation fees counted as ARR) that would reduce normalized ARR.
• 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.