Stage 1Confirmed
Problem framing
Confirmed summary
Students routinely miss shuttles due to inconsistent schedules and lack of ETA visibility. Peak-hour demand creates long lines and uncertainty about seat availability.
scored-Stage score 78
This is a read-only example of the report IdeaSense generates after a full structured review. It is designed to make the decision legible, not just verbose.
Read-only preview
Campus Shuttle Companion
Decision band
Proceed with guardrails
Total DVF score
74
Risks flagged
3
Insight report
Below is the same report surface used to summarize scores, risks, evidence quality, and next-step recommendations.
Executive summary
This opening distills the core decision, the evidence behind it, and the signals that matter most.
Decision-ready synthesis across all stages.
The shuttle companion addresses a clear commuter pain point with visible demand signals from pilot campuses. Proceed with a staged rollout focused on campuses that expose reliable transit data. Prioritize ETA reliability, then layer on reservations and demand analytics.
Rule-based signals computed from stage data.
Confidence: High (82% inputs covered)Composite DVF signal based on desirability, viability, and feasibility.
Summary of the dimension analysis.
Score: 78 - Strong commuter pain point with clear willingness to use.
Score: 70 - University procurement cycles add friction but budget exists.
Score: 74 - Integration risk is moderate; core build is straightforward.
74
Structured decision snapshot, rationales, risks, experiments, and evidence index.
The commuter pain point is clear and initial buyer access is plausible, but rollout should stay gated by transit feed reliability and buyer validation.
Next action: Run a campus operator pilot and GTFS feed spike before full buildout.
Repeated missed-shuttle scenarios indicate a concrete student pain.
Evidence gaps: Interview more accessibility riders before widening scope.
Institutional budget exists, but procurement timing and pilot conversion remain unproven.
Evidence gaps: Secure a written pilot price range from two operators.
The MVP is straightforward, while feed normalization is the main delivery risk.
Evidence gaps: Test a real GTFS feed before committing to reservations.
Early warning: Pilot campus cannot provide stable ETA feed updates.
Mitigation: Start with campuses that expose GTFS feeds and add manual upload tooling.
Early warning: Operator interest does not convert into a budget owner meeting.
Mitigation: Pilot through student services grants and convert after term results.
Success signal: At least 3 users describe the same painful shuttle scenario without prompting.
Linked risk: Problem evidence gap
Success signal: Live ETA data renders reliably with realistic feed updates.
Linked risk: Data integration risk
Evidence-layered business diagnosis across confirmed inputs, assumptions, inferences, unknowns, and gaps.
The project has a clear commuter pain point, plausible institutional buyer, and moderate integration risk. The next decision should focus on buyer validation and feed reliability.
None captured.
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Evidence-layered business diagnosis across confirmed inputs, assumptions, inferences, unknowns, and gaps.
Context
This section captures what was evaluated, how complete the inputs are, and when the report was generated.
Missing inputs, skips, and overall coverage.
Confidence: High (82% inputs covered)
No missing required inputs detected.
Findings
The narrative moves from problem to market to technology, keeping the logic behind the decision intact.
Confirmed summary
Students routinely miss shuttles due to inconsistent schedules and lack of ETA visibility. Peak-hour demand creates long lines and uncertainty about seat availability.
Confirmed summary
Campus transit operators are seeking digital engagement tools, but procurement cycles remain a bottleneck. Student ambassadors and pilot grants show early traction.
Confirmed summary
Core build is feasible using existing maps and push services. Biggest risk is normalizing inconsistent transit feeds across campuses.
Verification
External validation for high-priority claims with sources.
Evidence-backed checks for the highest-priority questions.
No verification data yet.
Validation
Concrete signals and short-cycle tests that back the market opportunity.
Signals and short-cycle validation tests.
Business model
A structured view of the assumptions that tie customer needs, value, and monetization together.
Core assumptions and focus areas.
Real-time shuttle visibility with demand-aware scheduling.
Exclusive integration with campus transit data feeds.
Risks and feasibility
Risks and technical feasibility highlight what could block delivery, so mitigation can be planned early.
Issues to track and mitigate.
Mitigation: Start with campuses that already expose GTFS feeds and add manual upload tooling.
Mitigation: Pilot as a student services grant and convert to annual contracts post-term.
Mitigation: Ship confidence bands on ETAs and prompt drivers to update status.
System sketch for the current implementation.
Conclusion
This closing summarizes the decision position and the immediate actions required to move forward.
Appendix
Reference details for audit, sharing, and record keeping.
Generated Feb 1, 2026, 4:00 PM
Campus Shuttle Companion
A lightweight app that helps students track campus shuttles, reserve seats, and crowdsource demand for peak routes.