Is the pain real?
Frequency, cost, delay, risk, and sponsor urgency.
Market signal → funded decision → working pilot
Find the AI use case worth funding. Then prove it in a controlled pilot.
Start from real market signals and company evidence. Rank what to fund, prepare, or reject—then prove the winner in a supervised pilot.
Advisors keep the client relationship, judgment, and fee. Operational teams start with one workflow—support, evidence assembly, or financial reconciliation. The starter is free after sign-up; full analysis opens after a short approval.
Possible
Generic support chatbot
Easy to imagine. Weak company-specific evidence.
Prepare first
Measure quote exceptions
Closes the evidence gap before automation.
Fund next
Estimator copilot
High pain · accessible history · scarce judgment
The recurring failure
Twenty plausible ideas enter the room. None can answer “why this, why now?”
A brainstorm can create momentum. It cannot prove that a workflow hurts enough, the evidence is usable, the owner will act, or the outcome strengthens anything competitors cannot copy.
Can it work here?
Data access, systems, controls, owners, and exceptions.
Is it hard to copy?
Company data, scarce judgment, customer trust, and reusable strengths.
Use Case Foundry makes those questions the assessment, not an appendix added after funding.
For whom
One method, several ways in.
Rank AI opportunities across a company, or start where the problem is already known—each path shares the same evidence, controls, and pilot boundary.
For advisors and consultants
Win and deliver stronger client engagements
Begin before the workshop, preserve your method, and give the client a clear path from recommendation to pilot—under your brand and fee.
Use Foundry with clients For investment and portfolio teamsDiligence the AI opportunity and risk
Turn public evidence into an IC-ready read: value sources, questions for management, and a first-100-days agenda.
See the diligence path For transformation teamsPrioritize the portfolio
Compare quick wins, larger bets, required preparation, and evidence gaps before pilots compete for budget.
See the transformation path For functional and operations leadersPilot a tested agent path
Start with support copilot, evidence assembly, or financial reconciliation—each with explicit limits and acceptance checks before reliance.
See the functional pathOne repeatable method
From market signal to a pilot plan you can fund.
Each step produces evidence for the next. Public signals start the inquiry; you decide what becomes company truth and what is ready to move forward.
Build the pre-assessment
Start with company-specific public evidence. If that first read is thin, external pressure signals are added in the background.
Understand the company
Validate how work really happens: the problems, the owners, the data, and the strengths that make it theirs to win.
Find supported use cases
Match the evidence against proven AI opportunity patterns, not a generic idea list.
Test value and readiness
Estimate value, effort, and payback, and confirm the systems and data can support it.
Prioritize the decision
Rank each opportunity as fund now, prepare first, or reject, with sources and written reasons.
Plan adoption and simulate
Sequence the rollout, name the owners, and test the agent on realistic cases, never on live records.
Hand off a supervised pilot
One workflow, clear success measures, and reviewers who approve every action. Nothing runs without a named owner’s sign-off, and you can stop at any time.
A decision, not a wish list
Follow one opportunity from evidence to action.
Meridian Fab is illustrative: slow quotes, overloaded estimators, years of pricing history, and customer pressure for faster, traceable bid packages. The same method applies whether you advise a client or run the workflow yourself.
Signal
Quote delays threaten response time
Dated customer and market evidence creates a question to test, not an automatic recommendation.
Operating proof
Scarce estimator judgment is the bottleneck
Interviews and records confirm measurable pain, an owner, accessible history, and a required human sign-off.
Decision
Pilot the estimator copilot
The value is high, the scope is clear, and the workflow can preserve company-specific judgment instead of replacing it.
Handoff
Plan the change and prove it first
The adoption plan, simulation results, and starter code travel with the decision, so delivery starts building instead of guessing.
What travels with the decision
Every stakeholder gets the material they need next.
Pre-assessment working paper
Initial company map, public signals where relevant, illustrative AI directions, and focused questions for the first meeting.
Ranked AI roadmap
Quick wins, larger bets, required preparation, evidence gaps, and the generic ideas that were deliberately dropped.
Adoption plan
Shared foundations, accountable owners, controls, readiness questions, and the recommended rollout route.
Simulation report
How the agent handles routine, tricky, ambiguous, and unsafe cases, tested on realistic data instead of live records.
Diligence memo
Risk and advantage findings, questions for management, possible sources of value, and a first-100-days agenda.
Pilot handoff package
Agent design, test suite, human approval rules, pilot plan, integration assumptions, and downloadable starter code.
Why the answer is easier to trust
The recommendation keeps its receipts.
Automation drafts; people decide. Machine-drafted facts stay suggestions until someone accepts them, every score keeps its reason, and fixed checks, not model confidence, decide what moves forward. Private evidence stays in the environment you choose.
SourcesDated and reviewable
Machine-drafted factsSuggestions until accepted
Priority scoreWritten reasons for each part
Evidence gapsNamed questions and owners
ReadinessFixed pass-or-fail checks
When you are ready to build
Three supervised pilot paths.
Once one use case is funded, hand it to a tested pilot path with explicit limits and environment-specific acceptance checks. Pilots run in your own cloud, and reviewers approve every action.
For support teams
Support copilot
Answers support questions from approved content, cites its sources, and respects access controls.
Review the support path For compliance teamsEvidence assembly
Assembles reviewable evidence packets and flags the gaps. Your team keeps the final word on every control.
Review the evidence path For finance teamsFinancial reconciliation
Matches records, queues exceptions, and keeps a lasting audit trail. The agent proposes; a named reviewer approves.
Review the finance pilotValidated in controlled live checks: the finance pilot runs on Google Cloud Run with PostgreSQL, signed-in access, secure secrets, and monitoring. It never posts journals or moves money. Packages for AWS, Azure, and Kubernetes are available for customer testing, and every environment gets its own validation before launch.
How you buy
Start free. Pay when the engagement is real.
Run the pre-assessment on a real company at no cost—your own or a client's. When the work turns into a paid engagement, Use Case Foundry is quoted separately, so an advisor's own fee stays intact.
Start here
Pre-assessment starter
Free
Advisors bringing one client or prospect
For advisorsAdvisor white-label founding terms
Founding: $495 / named client
Advisors who set and own the client engagement fee
Founding offerValidation workshop
Free (bounded validation)
Investment, portfolio, or transformation teams buying directly
After the first jobAdvisor workspace
Quoted after the first paid job
Repeat assessments across clients or portfolio companies
Buying directly for your own company? Validation workshop is free in the founding offer; Opportunity sprint starts at $495. Advisors set and own their client fee.
Start with one real decision
Bring one active client—or one support, evidence, or reconciliation workflow.
Tell us who it is and what you need to deliver. We reply by email, usually within one business day.
Create a free account for the pre-assessment starter. Full analysis opens after a short approval.