QED
Quod Erat Demonstrandum - “That Which Is Demonstrated”
AI Development Patterns: A Practitioner's Guide
Evidence-based patterns for AI-assisted development. Built from real production systems, tested in client environments, validated with measurable outcomes.
Access the full guide with 50+ patterns, learning paths, and case studies
The Practitioner's Challenge
When you're responsible for delivering AI solutions to clients, every pattern recommendation carries professional liability. QED fills the gap between impressive demos and production-ready implementations that actually work in enterprise environments.
You're accountable for security decisions, architecture choices that scale, risk assessments that prevent failures, and performance guarantees that meet enterprise expectations.
🎯 Navigate by Risk Level
🏢 Navigate by Context
🔧 Navigate by Domain
The QED Methodology
Evidence-based pattern organization with systematic validation:
Comprehensive intake of industry patterns and frameworks
Professional evaluation with risk assessment matrices
Only patterns validated in production with documented outcomes
What Makes QED Different
Every pattern backed by documented client outcomes
Systematic evaluation for enterprise architecture decisions
Patterns validated with accountability in mind
Security, privacy, and compliance considerations
Documented failure modes and known constraints
Specific metrics and validation criteria
Who This Is For
Delivering AI solutions to enterprise clients
Evaluating AI integration strategies
Building production AI architectures
Creating AI-powered client applications
Managing client AI projects
Designing compliant AI systems