Problem: "Our SaaS platform has 40% user churn after the free trial"

Immediate Analysis:

Language: "Churn" vs "didn't convert" - framing affects solution approach
Mathematics: 40% = 2.5x industry average (16%), statistically significant
Engineering: Check onboarding flow completion rates, feature adoption metrics
Legal: Review trial terms, auto-billing compliance, cancellation process
Strategy: Competitor pricing analysis, value proposition alignment
Risk: Revenue impact $2M annually, reputation damage, investor confidence

Step 1: Domain Decomposition (5 min)

• Language analysis: Define terms, identify ambiguities
• Mathematical scope: What can be quantified?
• Engineering constraints: Technical limitations/possibilities
• Legal boundaries: Compliance requirements, liability
• Strategic context: Market position, competitive dynamics

Step 2: Multi-Expert Deep Dive (15 min)

• Journalist investigation: Who, what, when, where, why, how?
• Analyst pattern recognition: Historical data, trend analysis
• Engineer root cause analysis: Systems thinking, failure modes
• Consultant benchmarking: Industry standards, best practices
• Auditor verification: Data integrity, process compliance

Step 3: Synthesis & Solution Architecture (10 min)

• Cross-domain insights: Where do findings intersect?
• Risk-weighted options: Probability × impact matrix
• Implementation roadmap: Dependencies, timeline, resources
• Verification framework: How will we know it worked?

Example 1: Team Performance Issue Input: "Our development team velocity dropped 30% this quarter" Output:

Journalist Analysis: When did decline start? (Post-hiring surge in July)
Mathematical Model: Velocity = f(team_size, complexity, experience)
Engineering Review: Code quality metrics stable, deployment frequency down
Communication Audit: Daily standup participation dropped from 95% to 60%
Legal Check: No compliance issues affecting delivery
Strategy Impact: Q4 feature delivery at risk, customer commitments
Root Cause: Onboarding overhead + knowledge silos in new hires
Solution: Pair programming mandate + async standup format + documentation sprint

Example 2: Product Feature Decision Input: "Should we build AI-powered search for our e-commerce platform?" Output:

Market Analysis: 73% of competitors offer AI search, 2.3x conversion lift
Engineering Estimate: 6 months, $400K, requires ML infrastructure
Legal Review: EU AI Act compliance needed, data usage consent updates
Mathematics: ROI = 18 months at current traffic, 12 months with 20% growth
Risk Assessment: Technical complexity high, competitive necessity medium
Aesthetic Code Review: UI/UX patterns for AI search, user experience flow
Decision: Build minimal viable AI search, outsource ML model initially

Example 3: Crisis Management Input: "Data breach affecting 50K customer email addresses" Output:

Legal Triage: GDPR notification required within 72 hours, potential €20M fine
Engineering Forensics: Attack vector via deprecated API, scope contained
Communication Plan: Customer notice within 24 hours, press statement ready
Risk Assessment: Churn probability 15%, reputation impact 6 months
Audit Trail: Document all response actions for regulatory compliance
Strategy: Transparency approach, security investment announcement
Truth Verification: Confirm breach scope, validate containment measures
Action Plan: Legal notice filed, customers notified, security audit scheduled