- Project setup
Give your AI coding tool the context it needs. Your stack, project structure, working commands, and rules in one place.
- Architecture
A connected view of your product: data, screens, integrations, and infrastructure. Decisions explained, trade-offs made explicit.
- Product scope
What the first version should do, who it serves, and what can wait. User journeys and acceptance criteria make the scope concrete.
- Security plan
Potential threats and recommended safeguards for your product. A plan for safer implementation, not a certification or a substitute for a code audit.
- Launch strategy
Positioning, audience, channels, and a practical 90-day launch plan. A starting point to test against the market.
- Build instructions
A sequence of focused prompts to guide your AI coding tool through the build. Each step explains what to create and how to check it.
- Task checklist
Build tasks in dependency order. Keep track of what is done and what needs to happen next.
- Design direction
A shared visual language: colors, typography, spacing, and component guidance for a consistent interface.
- Industry insights
Your industry’s workflows, buying dynamics, and adoption risks. Specific questions to investigate before committing to the build.
- Compliance map
When your idea touches regulated data or markets, get relevant obligations mapped to implementation tasks. Confirm legal requirements with a qualified adviser.