Underwrite AI claims with evidence, not slogans.
Use Case Foundry helps diligence teams test whether a company can actually win with AI based on assets, data, workflows, constraints, and execution readiness.
Why AI claims are hard to underwrite
The common failure mode is not a lack of AI ideas. It is a lack of company-specific evidence for choosing which ideas deserve attention.
- Pitch decks say AI-enabled, but the evidence behind defensibility is thin.
- Automation potential gets confused with durable moat or customer advantage.
- Data access, proprietary workflow knowledge, and execution constraints are hard to compare quickly.
What diligence can test
Use Case Foundry keeps the conversation grounded in workflows, data, pains, constraints, feasibility, moat, and evidence quality.
A moat-grounded assessment of which AI bets the company is positioned to win.
A clear split between feasible automation, strategic advantage, prerequisites, and gaps.
Questions for management that expose missing evidence before investment or acquisition decisions.
A diligence memo for AI readiness
Use Case Foundry turns company evidence into a structured view of impact, feasibility, moat, quality, and readiness so diligence teams can challenge AI narratives with specifics.
Pilot the grounded workflow
Start where pain, data access, and human review make the first experiment credible.
Invest where the company has advantage
Prioritize candidates backed by proprietary data, scarce expertise, or reusable abstractions.
Close what changes the decision
Turn missing facts into interviews, metadata checks, redacted samples, or instrumentation work.
Why the recommendations are easier to defend
The method makes the reasoning inspectable before budget, pilots, or diligence decisions depend on it.
Moat scoring rewards proprietary data, scarce expertise, reusable abstractions, and switching costs.
Generic ideas are demoted before they become polished but weak investment claims.
Evidence gaps become diligence questions for management, customers, and technical owners.
Built for sensitive evidence work
Use public pages, notes, metadata, aggregates, redacted samples, or controlled LLM endpoints. Human reviewers decide which facts enter the model.
Private discovery without forcing raw records into the workflow
Auto-map and evidence extraction propose changes, but scoring only changes after a reviewer accepts the facts.
Build an AI roadmap you can defend
Use a sample company now, or reach out to discuss the assessment workflow for your team.