AI tools promise 10x productivity. Most teams see 10%. I built Auralink—a complete EV charging platform with 319 microservices—in 2 months. Solo. Using a methodology that actually works. That's what I teach.
Your team has Copilot subscriptions. They're not 10x faster—maybe 20% at best.
AI code suggestions create as many bugs as they fix. Net productivity gain: zero.
Nobody knows how to review AI-generated code effectively. Quality is suffering.
The 'AI development' workflow is ad hoc, inconsistent, and unteachable.
The same approach I used to build a complete EV charging platform in 2 months. Systematic. Teachable. Proven in production.
Evaluate current AI tool usage, identify gaps, measure baseline productivity metrics
Introduce systematic approach: prompting patterns, review workflows, quality gates that actually work
Hands-on workshops using your actual codebase and real projects—not toy examples
Integrate methodology into daily workflows, measure improvements, iterate and refine
The exact methodology used to build a complete EV charging platform (319 microservices, ~20 AI agents) in 2 months. Not theory—a battle-tested workflow that transforms how developers work with AI tools.
Your team has AI tools but isn't seeing the productivity gains. You want systematic methodology, not random tips. You believe in capability transfer—not permanent consultant dependency.
Most teams use Copilot for autocomplete—that's maybe 20% of what's possible. The Auralink methodology covers architectural prompting, code review workflows, test-driven AI development, and context management. It's the difference between having the tool and having the methodology.
The methodology is stack-agnostic. I've applied it across Python, TypeScript, Go, React, and various frameworks. The principles of effective AI-augmented development transfer across languages and platforms.
We establish baseline metrics before training—lines of code, PRs merged, bug rates, time-to-feature. Then track the same metrics post-training. Typical improvements are 3-5x for experienced developers who commit to the methodology.
Poorly used AI tools create debt. The methodology includes review patterns, testing strategies, and quality gates specifically designed for AI-generated code. Teams often see quality improvements because the methodology enforces practices they should have been doing anyway.
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