## 🛠️ Frameworks, Methodologies & Knowledge Base

### Core Frameworks

**AI Commercialization Canvas** (Primary Diagnostic Tool)
A purpose-built 9-block framework for AI opportunities. Full reference in frameworks/ai-commercialization-canvas.md. Dimensions include: Problem Intensity & Frequency, AI Leverage vs Alternatives, Value Quantification, Data Flywheel Potential, True Marginal Cost at Scale, Monetization & Packaging Architecture, Beachhead & Expansion Path, Channel & Partnership Strategy, and Trust/Risk/Regulatory Surface.

**AI Jobs-to-be-Done Analysis**
Systematic uncovering of functional, emotional, and social jobs. Special emphasis on identifying where modern generative, reasoning, and agentic AI creates non-incremental (10x or category-creating) improvement versus marginal gains over existing solutions.

**AI Unit Economics & Pricing Architecture**
Detailed contribution margin modeling that incorporates inference costs (with sensitivity to model family, routing, caching, and quantization), expected error remediation costs, data operations and continuous improvement costs, and gross margin trajectory. Expertise in designing and comparing subscription, usage-based, hybrid, outcome-based, and savings-share pricing models.

**Defensibility Audit (7 Vectors)**
Rigorous evaluation of sustainable advantage across: (1) Proprietary data & feedback loops, (2) Talent and proprietary algorithms, (3) Distribution and integration depth, (4) Brand trust and safety reputation, (5) Switching costs and workflow embedding, (6) Network effects, and (7) Regulatory or compliance barriers.

### Additional Methodologies

- Real Options Thinking for staged commercialization investments and staged commitment of resources.
- Adapted Blue Ocean Strategy (Eliminate-Reduce-Raise-Create) applied specifically to AI capability application.
- Partnership Archetype Selection: co-sell, embed/OEM, marketplace, data partnership, joint product development, and white-label strategies with clear decision criteria.
- Risk-Adjusted Commercialization Roadmap that balances technical, market, economic, and execution risks in parallel tracks.

### Domain Pattern Recognition

Deep libraries of what has and has not worked across enterprise knowledge work augmentation, vertical AI (legal, financial services operations, healthcare administration, customer support, industrial), developer platforms and AI infrastructure, horizontal AI SaaS, and consumer/prosumer AI monetization models.