Frame
Map the thesis, revenue model, and AI-sensitive assumptions.
Diligence
Put AI in the deal model.
The decision
Does AI strengthen the thesis, change the conditions, or create a reason to walk?
The gap
We test how AI changes the company’s economics.
In a labor-driven business, AI moves both sides of the underwriting case: the revenue at risk and the margin available.
- MarketCompetitive adoption, pricing pressure, and business-model exposure.
- ReadinessSystems, data maturity, integrations, talent, and management capacity.
- EconomicsRevenue at risk, margin opportunity, capital required, and timing.
- ExecutionDependencies, sequence, build-versus-buy, and the critical unknowns.
What you receive
A verdict for the IC memo.
- Readiness viewTechnology, data, and organizational capability against the thesis.
- EBITDA modelBase, upside, and downside across the hold period.
- Initiative mapOpportunities ranked by value, confidence, capital, and time.
- Deal verdictTailwind, manageable exposure, unresolved uncertainty, or severe risk.
EngagementDAY 1 DAYS 2–4 DAYS 5–6
Six days, run alongside the deal.
Runs in parallel with quality of earnings, commercial, and legal diligence, and starts from the CIM.
Underwrite
Test exposure, readiness, value levers, cost, and execution risk.
Decide
Deliver the model, priorities, verdict, and conditions.
Questions from sponsors
How is this different from technology diligence?
Technology diligence asks whether the stack is sound. We ask how AI changes the economics: revenue exposure, value creation, competitive position, capital required, and time to execute.
What do you need to start?
A CIM and a revenue breakdown by service line. Management materials, an org chart, and technology documentation accelerate the work, but the first pass can begin without them.
What if the answer is to pass?
Then we say so, with the evidence. The analysis is independent of the deal outcome.
Before the next close
Schedule a call →