Use Case Foundry
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For investment and portfolio teams

Test AI claims with evidence, not slogans—across the whole portfolio.

Use Case Foundry helps investment and portfolio teams test whether a company can win with AI based on its assets, data, workflows, limits, and ability to deliver. The same assessment works across companies, with written reasons ready for investment memos and operating plans.

Request an AI diligence read Review a sample diligence memo

The tension

Why AI claims are hard to underwrite

Pressure 1

Pitch decks say AI-enabled, but the evidence behind the claimed advantage is thin.

Pressure 2

Automation potential gets confused with an advantage customers value and competitors cannot easily copy.

Pressure 3

Data access, proprietary workflow knowledge, and execution constraints are hard to compare quickly.

Pressure 4

Every portfolio company needs an AI value-creation read, and ad hoc assessments don't compare across the portfolio.

Pressure 5

A polished agent demo says nothing about whether it can pass representative tests, a supervised pilot, or production checks.

The decision

What diligence and portfolio ops can test

Outcome 1

A first read of public evidence, customer pressure, and possible business priorities before management interviews.

Outcome 2

An assessment of which AI bets fit the company’s real strengths.

Outcome 3

A clear split between feasible automation, strategic advantage, prerequisites, and gaps.

Outcome 4

Questions for management that expose missing evidence before investment or acquisition decisions.

Outcome 5

The same repeatable assessment across every portfolio company, so AI value-creation reads are comparable.

Outcome 6

A clear readiness result—ready, needs evidence, or unsafe—for any agent claim under review.

A concrete output

A diligence memo for AI readiness

Use Case Foundry turns company evidence into a clear view of value, practicality, company advantage, evidence quality, and readiness. Investment and portfolio teams can challenge AI claims with specifics, including whether a proposed agent can pass representative tests and production-handoff checks.

What a deal or operating team should leave with

  • A memo that names the strongest possible sources of AI value and the evidence behind them.
  • A short list of management questions that would most change the recommendation.
  • A 100-day plan that separates quick validations from prerequisites and longer bets.
  • A comparison lens that makes company-to-company reads repeatable across the portfolio.

What the first 100 days should clarify

  1. Where the proprietary advantage really lives — data rights, workflow ownership, or scarce judgment.
  2. Which claims are generic — impressive demos with weak linkage to differentiated business outcomes.
  3. What management must prove next — data access, operating owners, pass thresholds, and rollout constraints.

What to avoid

Do not reduce the diligence read to a generic maturity score. The job is to identify which specific AI bets are grounded, what remains unproven, and what evidence would justify the next dollar of investment.

Why the answer holds up

Keep the reasoning attached to the recommendation.

1

Every inferred market priority keeps its dated public sources and remains a question until reviewed.

2

Moat scoring rewards proprietary data, scarce expertise, reusable abstractions, and switching costs.

3

Generic ideas are demoted before they become polished but weak investment claims.

4

Evidence gaps become diligence questions for management, customers, and technical owners.

5

Written score reasons make every ranking easy to review in an investment memo or operating plan.

6

Deployment readiness comes from reviewed evidence and fixed checks, not from the company’s AI marketing claims.

Related reading

Go deeper with resources

Review sample outputs, comparisons, and methodology pages to evaluate fit before a pilot.

Resource How you buy

Starting prices for single-company diligence; portfolio scans are quoted.

 Resource Demand intelligence method

How to form a source-linked outside-in view before management access.

 Resource Sample diligence memo

Review an illustrative memo, 100-day plan, and comparison lens.

 Resource Sample pre-assessment

Review a source-linked first read with optional outside pressure context.

 Resource Company-advantage review

Signals that make an AI opportunity worth funding.

 Resource Trust boundary

How private evidence and human review are handled.

 Resource Sample roadmap output

See how evidence gaps appear before funding decisions.

 Resource Agent deployment readiness

The fixed checks behind any claim that an agent is ready to deploy.

Other buyer paths

Explore related audience pages.

For AI & strategy advisors AI & strategy advisors

You keep the client relationship, judgment, offer, and fee. Use Case Foundry turns client evidence, interviews, and operating context into a ranked roadmap the sponsor can question and fund, then prepares the best opportunities for a pilot.

 For transformation teams Innovation & transformation teams

Use Case Foundry helps transformation teams compare opportunities using the same company evidence: workflows, data, problems, limits, practicality, advantage, and readiness. Ideas worth funding then move into clear packages for engineering.

 For functional leaders Functional leaders

Use Case Foundry helps finance, operations, revenue, support, and people leaders separate practical pilots from generic AI proposals—with approval rules, success measures, and starter code for ideas worth testing.

Next steps

Go deeper with resources

Starting prices for single-company diligence; portfolio scans are quoted.

How you buy Return home