Underwrite AI claims with evidence, not slogans — across the whole portfolio.
Use Case Foundry helps diligence and portfolio-operations teams test whether a company can actually win with AI based on assets, data, workflows, constraints, and execution readiness — one repeatable assessment per portfolio company, with written score reasons that drop straight into IC memos and value-creation plans.
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.
- Every portfolio company needs an AI value-creation read, and ad hoc assessments don't compare across the portfolio.
- A polished agent demo says nothing about whether it could clear a real eval, pilot, or production gate.
What diligence and portfolio ops can test
Use Case Foundry keeps the conversation grounded in workflows, data, pains, constraints, feasibility, moat, and evidence quality.
A zero-cooperation public-evidence read of customer pressure and likely internal priorities before management access.
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.
The same repeatable assessment across every portfolio company, so AI value-creation reads are comparable.
A deterministic readiness verdict — ready, needs evidence, or unsafe — for any agent claim under review.
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 and portfolio-operations teams can challenge AI narratives with specifics — including whether an agent claim would clear an eval suite and a production handoff gate.
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.
Every inferred demand mandate retains its dated public source chain and remains a hypothesis until reviewed.
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.
Written score reasons make every ranking inspectable in an IC memo or value-creation plan.
Readiness verdicts come from the graph and scaffold, not from the target company's own LLM narrative.
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.
Go deeper with resources
Review sample outputs, comparisons, and methodology pages to evaluate fit before a pilot.
Demand intelligence method
How to form a source-linked outside-in view before management access.
Sample discovery brief
Review a URL-first, source-linked pressure and mandate read.
Defensibility teardown
Signals that make an AI opportunity worth underwriting.
Trust boundary
How private evidence and human review are handled.
Sample roadmap output
See how evidence gaps appear before funding decisions.
Agent deployment readiness
The deterministic gates behind any deployable-agent claim.
Build an AI roadmap you can defend
Use a sample company now, or reach out to discuss the assessment workflow for your team.