Appendices: Templates and Checklists

Appendix A: Compliance Checklist

Regulatory compliance isn't optional -- it's a feasibility constraint. Use this checklist before committing to your MVP build. Each item represents a concrete requirement that could affect your architecture, timeline, or budget. Items marked with a red flag require immediate attention and may need legal consultation.

The checklist is organized by category, with the most universally applicable items first. Even if you're building a "simple" SaaS product, you'll likely need to address most items in the Data Privacy section. AI-specific items apply if your product uses any form of machine learning or large language models. Industry-specific items depend on your target market.

Appendix B: Pre-Mortem Template

Run this exercise with your team before making the Go/No-Go decision. The Pre-Mortem, developed by psychologist Gary Klein, is one of the most effective risk identification techniques available. It works by leveraging a cognitive quirk: people are better at explaining past events than predicting future ones. By imagining that failure has already occurred, you unlock insights that forward-looking risk analysis misses.

Preparation

• Participants: All co-founders and early team members. Include advisors if available. 3-8 people is the ideal group size.
• Time needed: 60-90 minutes for the full exercise.
• Materials: Sticky notes or shared digital document, timer, whiteboard or wall space for clustering.
• Facilitator: Designate someone to facilitate who can remain neutral and ensure all voices are heard.

Process

1. Individual writing (10 minutes): Each person independently writes 3-5 specific failure scenarios. No discussion during this phase. Encourage specificity: "We ran out of cash in Month 14 because our CAC escalated 40% faster than projected" is better than "We ran out of money."
2. Round-robin sharing (15 minutes): Go around the table. Each person reads one scenario at a time. No debating or dismissing. The facilitator captures each item on the whiteboard. Continue until all items are shared.
3. Clustering (10 minutes): Group similar failure modes together. Common clusters: financial failures, team failures, product failures, competitive failures, regulatory failures, timing failures.
4. Voting (5 minutes): Each person gets 3 votes. Vote for the failure modes you believe are most likely AND most impactful. Items with multiple votes are your highest-priority risks.
5. Mitigation planning (30 minutes): For the top 3-5 risks, discuss specific mitigation actions. Assign an owner and a deadline for each. Add these to your risk matrix from Chapter 8.

Common failure modes to seed the conversation if the group needs prompting:

Appendix C: Unit Economics Health Check Scorecard

Use this scorecard to quickly assess whether your unit economics are healthy, concerning, or in critical territory. Check your numbers against each row. Multiple "Warning" results indicate your model needs work. Any "Critical" result should be resolved before committing to your MVP build.

The benchmarks here are drawn from data published by SaaStr, OpenView, Lighter Capital, and other startup-focused research organizations. They represent broad industry averages -- your specific benchmarks may differ based on your vertical, target customer, and business model. Use them as starting points, not absolute standards.

Appendix D: AI Cost Benchmarks (2026)

These benchmarks represent market pricing as of early 2026. AI inference costs have been declining 50-70% annually, so these numbers will likely be lower when you read this. Use them as a starting point for cost modeling, but validate against current pricing from your specific providers.

The table below covers the major cost categories for AI-native products. Note the enormous range within each tier -- cost varies significantly based on provider, model size, and whether you're using the model's API or self-hosting.

Additional AI Cost Components

Appendix E: Feasibility Analysis Glossary

Key terms used throughout this playbook, defined with precision for founders who may be encountering them for the first time:

What You Walk Away With

• Regulatory Checklist: A comprehensive, actionable compliance assessment covering data privacy, AI regulations, legal foundations, and industry-specific requirements. Ready to use as a working document during your MVP build.
• Pre-Mortem Template: A complete facilitation guide for running pre-mortem exercises, including seeded failure scenarios across financial, team, regulatory, and competitive categories.
• Unit Economics Scorecard: A quick-reference assessment tool with industry benchmarks for seven critical metrics, color-coded by health status with clear explanations of why each metric matters.
• AI Cost Benchmarks: Current reference prices for all major AI cost categories (inference, embeddings, vector storage, fine-tuning), with optimization strategies and cost trend projections.
• Feasibility Glossary: Precise definitions of all key terms used throughout this playbook, serving as a reference document for team alignment and investor conversations.