What automatic means here
Many automated assessments make a questionnaire faster. Use Case Foundry automates the expensive work around the questionnaire: finding evidence, structuring it, testing it against opportunity patterns, identifying gaps, and rerunning the ranking as the evidence improves.
Three automatic discovery engines
1. Website and document auto-map
Provide a company URL, annual report, proposal, process note, CSV, spreadsheet, or document folder. Foundry extracts supported offerings, segments, projects, workflows, pains, skills, assets, and constraints into a structured draft.
2. Outside-in public-evidence scan
Foundry scans dated customer, channel, competitor, supplier, regulator, hiring, investor, and industry sources. It produces a source-linked discovery brief and likely-priority hypotheses before management interviews begin. Public signals never become verified company facts on their own.
3. Consent-first workflow discovery
Optional workstation capture drafts recurring workflows, system touchpoints, and bottlenecks from local activity metadata. It records no screenshots, keystrokes, or file contents; raw activity stays local and only redacted summaries enter review.
Automatic evidence, not automatic truth
Extraction and discovery create proposals. A person reviews what enters the company map. Deterministic triggers then test 52 opportunity patterns against accepted facts, while written Impact × Feasibility × Moat reasons explain the order. This boundary makes automation useful without allowing a plausible model output to silently become strategy.
From discovery to an actionable result
The output is not only a readiness score. It is a ranked portfolio of quick wins, strategic bets, prerequisites, dropped generic ideas, and exact evidence gaps—plus gated blueprints, evals, pilot plans, and starter code for selected winners.