Overview
The Strategic Decision Advisor applies structured analytical frameworks — payoff matrices, decision trees, SWOT analysis, stakeholder mapping, and scenario planning — to complex decisions. Unlike a generic AI chat, this tool renders visual frameworks, provides a clear recommended strategy with confidence scoring, and supports iterative "what if" refinement.
Whether you're deciding between job offers, negotiating a vendor contract, evaluating a market entry strategy, or navigating a team conflict, the advisor breaks the decision into structured components and surfaces insights that are easy to miss in open-ended thinking.
How It Works
- Describe your decision — Explain the situation, available options, and any constraints or priorities. Or load a sample scenario to explore.
- Set the context — Choose personal, professional, business, or negotiation to calibrate the analysis tone.
- Review five result tabs — Overview, Frameworks (payoff matrix + SWOT + decision tree + stakeholder map), Scenarios, Action Plan, and Assumptions.
- Refine with "what if" questions — Iterate on the analysis by asking follow-up questions that adjust assumptions.
Key Features
Overview Tab
- Recommended strategy with confidence score (0-100%)
- Key insight — the single most important finding
- Quick SWOT summary — visual 2x2 grid of strengths, weaknesses, opportunities, threats
- Game theory analysis (advanced mode) — game type classification, dominant strategy, expected value
Frameworks Tab
- Payoff matrix — Color-coded HTML table showing outcomes for each actor/option combination, with Nash equilibrium identification
- Decision tree — Interactive Mermaid.js visualization with probability branches and outcome nodes
- SWOT+ grid — Full 2x2 analysis with detailed items per quadrant
- Stakeholder map (advanced) — SVG quadrant chart plotting stakeholders by power and interest, color-coded by stance
- Risk matrix (advanced) — Risks assessed by likelihood and impact with mitigation strategies
Scenarios Tab
- Optimistic, realistic, and pessimistic scenarios with probability estimates
- Key drivers — what makes each scenario happen
- Second-order effects (advanced) — chain reactions the primary strategy could trigger
Action Plan Tab
- Step-by-step execution plan with timelines and ownership
- Contingencies — what to do if each step doesn't work as planned
- Chronological ordering — actions sequenced for immediate execution
Assumptions Tab
- Explicit assumption listing — what the analysis depends on
- Sensitivity factors (advanced) — which variables would most change the recommendation
Refinement Chat
- Ask follow-up "what if" questions to explore alternative scenarios
- Get updated confidence scores and revised strategies based on changed assumptions
Use Cases
- Business strategy — Market entry, product launch, pricing, competitive response
- Career decisions — Job offers, role changes, education investments
- Negotiations — Vendor contracts, salary negotiations, partnership terms
- Personal decisions — Relocation, major purchases, relationship decisions
- Team leadership — Resource allocation, conflict resolution, organizational changes
- Investment analysis — Risk/reward evaluation, portfolio allocation
From Demo to Production
This demo analyzes one decision at a time. A production deployment would add:
- Decision journal — Track outcomes of past decisions, compare predictions to results, calibrate future confidence
- Team collaboration — Multiple stakeholders contribute perspectives, vote on options, build consensus
- Integration with data sources — Pull financial data, market research, and competitive intelligence automatically
- Monte Carlo simulation — Probabilistic outcome modeling with thousands of iterations
- Real-time game theory — Multi-round negotiation tracking with adaptive strategy updates
- API access — Integrate decision analysis into existing business intelligence workflows
Real-World Challenges
| Challenge | Why It's Hard |
|---|---|
| Incomplete information | Real decisions rarely have complete data. The AI must reason under uncertainty and be explicit about what's missing. |
| Cognitive bias detection | Framing effects, anchoring, and sunk cost fallacy influence how scenarios are described. Production needs bias-detection guardrails. |
| Stakeholder modeling | People are unpredictable. Game theory assumes rational actors, but real stakeholders have emotions, hidden agendas, and inconsistent preferences. |
| Outcome attribution | After a decision is made, it's hard to tell if the outcome was due to the strategy or external factors. Calibration requires large sample sizes. |
| Ethical guardrails | Some "optimal" strategies are manipulative or unethical. The system needs guardrails against recommending deceptive tactics. |
Cost Estimates (Platform Deployment)
| Component | Starter | Growth | Enterprise |
|---|---|---|---|
| AI API (GPT-4o-mini / GPT-4o) | $30–150/mo | $150–600/mo | $600–2,500/mo |
| Decision journal storage | $5–20/mo | $20–100/mo | $100–500/mo |
| Real-time data feeds | $0–100/mo | $100–500/mo | $500–2,000/mo |
| Total monthly | ~$50–300 | ~$300–1,200 | ~$1,200–5,000 |
ROI Definition
- Primary metric: Decision quality improvement — measured by prediction accuracy against tracked outcomes
- Secondary metric: Decision speed — time from problem identification to action
- Break-even: Typically within 1 month for executive decision support tools
- Concrete example: If better vendor negotiation strategy saves 5% on a $1M contract = $50K saved vs ~$3K/year platform cost
Technology Stack
- AI Model: OpenAI GPT-4o-mini (basic) / GPT-4o (advanced)
- Backend: Next.js API route (serverless)
- Frontend: React multi-tab client with Mermaid.js for decision trees
- Visualizations: Pure HTML/CSS tables, CSS Grid, SVG, Mermaid.js — no heavy charting libraries
Want This for Your Business?
White-label deployment for consulting firms, executive coaching, venture capital, or enterprise strategy teams. Connects to your data sources for real-time market intelligence. A full deployment typically takes 2–4 weeks and starts at $4,000.
This demo uses GPT-4o-mini for analysis. Advanced mode uses GPT-4o for deeper game theory analysis, stakeholder mapping, and risk assessment. No decision data is stored.