Start with nothing but a company URL. Use Case Foundry automatically gathers dated public evidence, drafts a company evidence map, and discovers the AI opportunities your company is positioned to win. Every machine-drafted fact stays behind human review. The best opportunities become a complete build package: the plan, the safety tests, the pilot playbook, and real starter code your engineers can run.
See a bundled, finished demo in minutes. A real assessment usually builds evidence across multiple sessions before engineering receives a package. Built for advisors, transformation teams, and functional leaders who are done presenting slide decks.
Executives don't need another "top 20 AI use cases" deck. Engineers don't want a slide with no way to start. Everyone wants the same thing: to know which bets are actually winnable — and to walk away with something buildable.
Most AI assessments end when the meeting does. This one ends with a package your sponsors can fund and your engineers can open on Monday morning.
Meridian Fab, a custom fabricator, comes in with slow quotes, a few overloaded senior estimators, and years of pricing history nobody uses. The assessment doesn't hand back "use AI for quoting." It tells them what to pilot now, what to prepare first, what to explore, and what to drop — and why. Then see what happens when the best idea keeps going — all the way to an agent your team can build ↓
Use quote history and cost records to suggest margin, risk, and similar jobs before a senior estimator signs off.
Meridian has quietly perfected turning messy field measurements into priced, buildable specs. Package that know-how as a repeatable offer for adjacent markets.
Capture why quotes stall, which fields are missing, and where senior judgment changes the answer before automating more of the workflow.
Confirm turnaround cost, win-rate impact, and whether historical quote, BOM, and final margin data are usable for a first experiment.
Demoted because the evidence does not show proprietary support data, a costly support pain, or a defensible reason this company would win.
Meet Atlas Ledger. Their accounts-payable team loses days matching invoices by hand. Here's how that one pain point becomes an agent their engineers can start building — with a checkpoint at every step, so nothing gets trusted before it's proven.
The invoice-matching pain, the systems it touches, the data that exists, the person who's the bottleneck, and the audit rules that can't be broken — all on the table before any agent is proposed.
What the agent does, what it's allowed to touch, where a human must approve, and what it may never do on its own. Ledger postings stay draft-only until the controller signs off — by design, from day one.
New vendors, price mismatches, duplicate invoices, missing receipts, wrong currencies — plus checks that the agent refuses unsafe actions. Written before launch, not after the first incident.
Every checkpoint is scored by fixed rules, not by an AI grading its own homework. Atlas Ledger cleared 7 out of 7 test cases before it earned the green light.
Which systems to connect, how to roll out safely — watch first, supervise next, then go live — how to roll back, what to monitor, and who signs off on what.
One click exports real starter files: a working agent runtime, connections to set up, the test suite, and a README that tells your team exactly where to pick up.
One honest boundary: Use Case Foundry prepares everything for deployment — it never deploys into your systems, creates credentials, or acts without human approval. Your team stays in control of what goes live. See the agentic AI readiness gates ↓ · See the finished demos ↓
The app walks you through The Foundry Method one step at a time — no giant intake form, no workshop fatigue. Start light, sharpen the picture, decide with confidence, then take the winners all the way to code.
Start with a company URL. Dated public evidence from named customers, channels, competitors, suppliers, regulators, hiring, investors, and industry sources becomes a reviewable discovery brief. For consumer businesses, aggregate review and platform-policy sources replace individual customer claims.
Auto-map from your company's website and documents, including annual reports; upload a CSV/XLSX to get a live dashboard from the file; opt in to workstation capture to draft workflows from how your team actually works; load a sample company; or pick up a saved session.
Capture how work actually happens — the workflows, the pains, the data you have, the rules you can't break. A live preview shows the picture getting sharper as you go.
See your AI opportunities ranked and explained: quick wins, big bets, what to prepare first, and what to walk away from. Each card shows what evidence would strengthen it, and you choose which candidates move into Generate.
The strongest opportunities get a build plan, safety tests, and a pilot playbook. Fixed checkpoints — not AI enthusiasm — decide whether each one is ready to move forward.
Once the checkpoints clear, download real starter code: a working agent runtime, the test suite, and a README your engineers can pick up the same day.
The evidence map connects reviewed company facts and relationships to ranked AI opportunities, while keeping source coverage and unanswered questions visible. It is the clearest picture of how Foundry moves from evidence to action.
Scan public customer, channel, competitor, supplier, regulatory, hiring, investor, and industry evidence to see where pressure may be building. Review every signal before it counts; then connect accepted evidence to likely priorities and, for named accounts, reviewable defend, deepen, expand, or land plays.
Websites and documents tell you what a company says it does. Opt-in workstation capture drafts what your team actually does — the recurring workflows, the systems they touch, where scarce judgment slows things down — and feeds it into the same evidence map behind every recommendation. No workshops to schedule, no process docs to write. And unlike task-mining tools, it was designed so employees can say yes to it.
Each person opts in on their own machine and can stop at any time. Denylists exclude chosen apps, sites, and folders before anything is even summarized.
No screenshots, no keystrokes, no file contents. Activity is summarized on the employee's own computer, and only a redacted summary of workflow patterns is shared.
Capture proposes workflows, tools, and bottlenecks as suggestions in the same review panel as every other source. A person you choose accepts what enters the model.
A candidate workflow reaches your review queue only after it recurs across enough sessions and days. One-off activity never becomes a recommendation.
The same company can need a cost-reduction roadmap this quarter, a transformation narrative next quarter, and a diligence memo tomorrow. Use Case Foundry adapts framing without changing the underlying facts.
A startup, SMB, agency, scaleup, and enterprise should not receive the same AI plan. The assessment changes its questions and sequencing to match resources and operating maturity.
Prioritize cost, productivity, risk, customer experience, or innovation without losing sight of evidence quality and defensibility.
Separate ideas that can be piloted today from ideas that first need instrumentation, clean data, governance, or evaluation guardrails.
Shape discovery around finance, operations, revenue, support, people, or another functional owner while preserving a company-wide roadmap.
Patterns from similar companies help you ask sharper questions early on — guiding the conversation without copying someone else's roadmap.
Founder, transformation lead, functional VP, advisor, or investor — the same facts can be packaged for the person who needs to act.
Sooner or later, an executive asks: “Why this one? Why now? And what proves it?” With Use Case Foundry, the answer is already in the output.
Opportunities are grounded in workflows, pains, data, skills, assets, and constraints instead of industry averages.
A quick pilot and a defensible advantage are not the same thing. The roadmap makes that tradeoff explicit.
When the model is thin, missing evidence becomes the next interview plan. Candidate cards show the exact facts that would strengthen each opportunity.
Ideas without proprietary grounding are called out before they become polished recommendations.
Prerequisites such as instrumentation, regression tests, guardrails, simulation twins, and synthetic data appear before high-stakes automation.
The Foundry Method is an AI consulting framework that runs: Scan, Source, Evidence, Review, Prove, Build. Under the hood, 52 proven opportunity patterns test where AI creates real value for your company, Impact × Feasibility × Moat scoring explains the order, and fixed readiness gates decide what can move forward. Explore the full framework, see the user guide, or use the field-by-field reference.
Find workflows where automation, expert copilots, forecasting, scheduling, self-serve analytics, or dynamic pricing can remove real cost or delay.
Surface opportunities in finance close, supply chain, hiring, healthcare operations, compliance evidence, support escalation, deal desk, and marketing operations. Public financial reports can also trigger asset-utilization, working-capital, and margin-defense plays.
Identify robotics adoption for labor-intensive physical work, generative design exploration, simulation and digital twins before live changes, and scientific discovery grounded on proprietary experimental data.
Identify modernization, testing, security, observability, autonomous remediation for known repeatable fixes, vendor-exit, integration, and developer-productivity plays when the codebase or toolchain is the bottleneck.
Budget, regulation, risk, talent, and stack constraints are not just penalties. They reveal guardrails, instrumentation, synthetic data generation, and knowledge-capture work that make later bets possible.
Find reusable abstractions, proprietary data products, embedded AI, sales intelligence, and segment expansion — then separate what can start now from what needs data capture, digital twins, regression tests, monitoring, or human review before it is safe to scale.
Not a report — a working package. Enough structure to compare your options, enough evidence to defend the decision, and for the winners, everything your team needs to start building.
Quick wins, big bets, what to prepare first, and what to drop — each tied to your workflows, your data, your people, and your constraints.
Easy ideas aren't always worth funding. We highlight where you hold proprietary data, scarce expertise, or an edge your competitors can't copy.
If an idea would fit any company in your industry, it gets demoted — before it turns into a convincing but weak recommendation.
What the agent does, what it's allowed to touch, and where humans stay in charge — earned by evidence, not enthusiasm.
Clear instructions, tool definitions, and permissions scoped to the minimum access the agent needs. No guesswork about what was meant.
The happy paths, the ugly edge cases, and the "never do this" checks — plus the bar the agent must clear before anyone trusts it.
Every blocker becomes a concrete ask — who needs to approve, what data to sample, what access to grant — with shortcuts back to the builder before the pilot, not during it.
Upload a CSV or spreadsheet and get deterministic findings, a file-grounded AI use case, a live dashboard, and a downloadable one-pager before modeling the whole company.
Opt-in workstation capture drafts workflows, tools, and bottlenecks from how your team's computers are actually used — redacted and local-first, with every draft reviewed by a human before it enters the model.
Check the candidates you want to take forward, use Top 5 or All when you need speed, and keep the rest as analyze-only context.
Who tests the agent, on what data, in what order — with monitoring in place and clear criteria for when to stop.
Which systems to connect, how to roll out in stages, how to roll back, what to watch, and who signs off on what.
A working agent runtime, the connections to configure, the test suite, and a README — with anything unfinished flagged honestly, not hidden.
Ready, needs work, or unsafe — every verdict comes from fixed rules and your evidence. The AI can explain a verdict; it can't change one.
Impact, feasibility, advantage, and risk are all visible. Stakeholders can push back on the ranking — which is exactly why they end up trusting it.
Work from summaries, redacted samples, or your own private AI endpoints when raw records shouldn't leave the building.
Open or download a self-contained HTML exhibit linking company facts, source classes, cited financial signals, open gaps, and ranked opportunities — viewable offline without the app.
Save your work, export your results, and build the picture across interviews — instead of losing the thread after one workshop.
Three sample companies come with the full journey already completed — every checkpoint passed and starter code ready to download. Viewing and downloading the bundled packages needs no API key. Running an exported agent against real systems still requires your own LLM endpoint, credentials, and technical review.
Open the app, load any bundled deployment demo, and download the finished package — it's already built, so there's nothing to wait for.
Built for the people on the hook for AI decisions — the ones who have to say where the budget goes, defend why, and hand the winners to someone who can build them.
The problem: every client engagement turns into a custom deck, and the thinking walks out the door with it.
What changes: a repeatable assessment you run with every client — ending in recommendations they can defend and an agent package their team can act on. Your clients stop asking "now what?"
Explore advisor use caseThe problem: "AI-enabled" claims are easy to make and hard to underwrite — and every portfolio company needs an AI value-creation read, not just the next deal.
What changes: a repeatable, explainable read on whether each company can actually win with AI — written score reasons that drop straight into IC memos and value-creation plans.
Explore portfolio & diligence use caseThe problem: too many AI ideas, not enough confidence about which deserve funding.
What changes: a portfolio that balances quick wins against big bets — and the winners arrive with everything implementation needs to start.
Explore transformation use caseThe problem: AI proposals land on your desk disconnected from how your team actually works.
What changes: you see which opportunities are real for your workflows — with approval gates, success metrics, and a first version your engineers can run.
Explore functional use caseBrowse real sample outputs, read how the method works, and grab templates you can use in your own discovery work — no signup required.
Browse all guides, teardowns, comparisons, templates, and FAQs in one place.
See how public-evidence demand-chain briefs frame market pressure before a discovery call.
Explore the six-step AI consulting framework that turns evidence into a gated build package.
See how Foundry automates evidence gathering while people control what becomes truth.
See how quick wins, strategic bets, prerequisites, and evidence gaps are sequenced in practice.
The full walkthrough: from first assessment to downloading an agent your team can build on.
Understand why brainstormed lists are not enough for funding decisions.
Understand the scoring logic, evidence boundary, and review controls behind recommendations.
Use better questions to gather workflow, pain, data, and risk evidence.
Answer common questions about data readiness, private evidence, and output quality.
Use Case Foundry never touches your systems, never creates credentials, and never deploys anything on its own. It prepares everything your team needs to deploy responsibly — and leaves the go-live decision where it belongs: with you. Read the trust boundary guide.
The AI can suggest facts about your company — but a person you choose reviews and accepts every one before it counts.
Describe your data instead of uploading it. Summaries, redacted samples, and plain observations are enough to get a real answer.
Capture is opt-in per employee, raw activity stays on their machine, and only redacted workflow summaries are shared — with a human approving every fact that enters the model.
No credentials created, no infrastructure touched, no systems called. Any action that writes or sends stays behind human approval at every stage.
Stricter environment? Point everything at your own private, company-controlled AI endpoint — nothing has to leave your network.
Scores, gaps, dropped ideas, and every readiness verdict stay visible. Export the evidence and provenance map as a self-contained HTML exhibit so stakeholders can challenge the roadmap without needing app access.
Every signal keeps its dated source link and expires when stale. A person decides what counts, and outside-in priorities can sharpen ranking but never create a project on their own.
A tool that approves everything is a tool you can't trust. Here are the three verdicts an agent can earn, and what each one means:
Wondering whether this fits your practice, your team, or your portfolio? Have a question about sensitive data or enterprise rollout? We're happy to talk it through.
See a finished agent in minutes with the built-in demos — or tell us what you're working on and we'll help you scope a pilot.