AI Coding Consultancy
Structured program to implement AI-first coding practices and increase engineering velocity.
Program Outcomes
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Engineering AI Adoption
Active usage across development team
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Automate Routine Challenges
Recurring development task automated
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AI-Assisted Feature Development
Complete feature delivered with AI
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Culture of Innovation
AI-first practices integrated into team
Proven AI Adoption Expertise
Real-world experience helping engineering teams successfully adopt AI-first development practices with measurable results
Enterprise AI Workshops
Led AI coding workshops for enterprise organizations, demonstrating practical AI integration and business value
Key Achievements:
Interactive demonstrations of AI-first development workflows
ROI analysis and adoption roadmap development
Team readiness assessment and change management strategies
Executive stakeholder alignment on AI adoption benefits
AI-Ready Code Quality Systems
Established code quality committees to create AI-consumable standards, transforming requirements into AI instructions and automated review systems
Key Achievements:
Converted code standards into AI instruction formats
Built automated code review systems using AI-readable guidelines
Created committee structures for AI/human collaborative quality control
Developed feedback loops between AI outputs and quality requirements
AI Architecture Innovation
Pioneered AI instruction architectures and frameworks for consistent, high-quality code generation
Key Achievements:
Developed AI prompt engineering methodologies
Created reusable instruction templates for coding assistants
Implemented context-aware AI guidance systems
Built quality assurance frameworks for AI-generated code
AI-First Project Leadership
Successfully delivered complex projects using AI-first methodologies with measurable business outcomes
Key Achievements:
WCAG 2.1 accessibility compliance through agentic AI processes
Right-to-Left internationalization: 100+ bugs resolved with 2 engineers
Hybrid AI/human architecture for safe feature development
Phased engineering approach preventing AI hallucination risks
For Engineering Teams & Directors
This program is designed to amplify your team's capabilities, not replace them. The goal is achieving more impact with your existing talent — delivering richer features, implementing better practices, and creating opportunities for engineers to work on higher-value problems. When your team can build faster and solve complex challenges more effectively, everyone wins: engineers grow their skills, businesses achieve ambitious goals, and innovation accelerates.
Program Timeline
Discovery & Strategy
Tooling Setup
Proof of Concept
Quality Standards
Pilot Project
Day0 Project Design
Iteration & Scaling
Sustaining Adoption
Progress
0%
Phase 1
Discovery & Strategy
Focus: Understand business, codebase, and team workflows
Key Benefit
Clear vision for AI adoption with executive buy-in
Deliverables
Stakeholder interviews & team surveys
AI Adoption Strategy Document
Success metrics framework
Pilot feature identification
Impact
Roadmap of what will be achieved by end of program
Phase 2
Tooling Setup
Focus: Select and configure AI tools for immediate use
Key Benefit
Engineers ready to use AI in daily workflow
Deliverables
GitHub Copilot + VSCode configuration
Claude 4 Sonnet integration
Secure MCP server setup
AI Tooling Setup Guide
Impact
Reduced setup friction for future hires
Phase 3
Proof of Concept
Focus: Demonstrate AI's practical value with real work
Key Benefit
Proof AI can navigate existing codebase
Deliverables
2-3 real tickets completed with AI
Process documentation
'Onboarding with AI' case study
Team confidence building
Impact
Increased confidence in AI's delivery capabilities
Phase 4
Quality Standards
Focus: Formalize AI-first quality standards
Key Benefit
Consistent quality across AI-generated code
Deliverables
Collaborative Code Standards Document
Code Excellence Committee formation
AI-augmented code review process
Quality benchmarks
Impact
Reduced review burden for senior engineers
Phase 5
Pilot Project
Focus: AI-assisted development at scale
Key Benefit
First project delivered using AI-first engineering
Deliverables
Small/medium project execution
AI-consumable task breakdown
Continuous feedback loops
Real-world validation
Impact
Engineers gain confidence in AI task management
Phase 6
Day0 Project Design
Focus: Adopt Day0 principles for project scaffolding
Key Benefit
Faster initial project scaffolding
Deliverables
Day0 vs MVP workshop
AI-friendly project splitting
AI-First Project Playbook
Repeatable framework
Impact
Reduced technical risk with AI contributions
Phase 7
Iteration & Scaling
Focus: Learn from pilots and refine adoption
Key Benefit
Engineers actively refining AI workflows
Deliverables
Team feedback collection
Standards document iteration
Next project identification
Process optimization
Impact
Institutional learning baked into development culture
Phase 8
Sustaining Adoption
Focus: Ensure long-term competitiveness
Key Benefit
Sustainable AI-first development culture
Deliverables
Refined AI review strategy
Long-term adoption plan
Final presentation & roadmap
Internal champion training
Impact
Company positioned for continuous improvement
Day0 vs MVP: The Foundation Difference
Understanding the strategic difference between user value and developer velocity
MVP (Minimum Viable Product)
For users and business validation
A functional product with just enough features to validate business value with end-users. Focus is delivering customer value as fast as possible.
Customer-focused feature set
Business value validation
Market feedback collection
Revenue generation potential
Day0 (Developer Foundation)
For builders and AI concurrency
A working project shell designed to maximize developer and AI concurrency. Focus is building scaffolding for safe, productive parallel development.
Deployable from the start
Clear separation of concerns
AI-friendly architecture
Risk containment & boundaries
"MVP is for users. Day0 is for builders."
Day0 establishes a working foundation that's deployable, testable, and structured so both humans and AI can build in parallel.
Ready to Transform Your Development Team?
Start your AI coding adoption journey with me, to supercharge your team's development capabilities and get more quality with what you already have.