Human Expertise. AI Acceleration. Transparent Governance.

AI-Assisted Delivery at MOC Global

"No matter the AI you choose, the responsibility for the results remains in your hands."

At Master of Code Global, AI is applied as a quality multiplier, not just a speed tool.
We embed AI across discovery, engineering, and quality workflows — and reinvest efficiency gains into deeper quality layers: higher test coverage, better documentation, and more thorough edge-case handling.
The result: more robust deliverables within the same investment, with ownership, accountability, and release authority remaining fully human-owned.

How AI Strengthens Our Delivery

Accelerated Understanding (Discovery & Design)

  • AI synthesizes large volumes of input early, enabling clearer specs from day one and fewer back-and-forth clarification cycles.
  • Tools include Firefly (structured insight extraction), GitHub SpecKit (early technical drafts), and NotebookLM (living knowledge repositories).
📊 Industry Research
20-30% faster requirements synthesis; 28% shorter development cycles when AI is used from project inception.

Engineering Acceleration (Development)

  • AI acts as a pair-programming layer — time saved is reinvested into writing more tests, better documentation, and handling edge cases.
  • Tools: Cursor, GitHub Copilot, Claude, Grammarly, and Glia AI for coding, reviews, and documentation.
  • All architecture decisions, code reviews, and production readiness remain human-led.
📊 Industry Research
35% fewer code defects; 60% faster documentation; 40% less code duplication. Efficiency gains flow into quality layers.

Expanded Confidence (Testing & Quality Engineering)

  • AI enables dramatically broader test scenario coverage — what previously achieved 70% coverage now reaches 90%+ in the same timeframe.
  • Stack: LLM-based Sim Users, Langfuse observability, Playwright for deterministic testing, Promptfoo & Giskard for AI quality validation.
  • Final QA approval and release decisions are always human-led.
📊 Industry Research
Test coverage: 75% → 92%; 35% better defect detection; 60% faster regression cycles. More bugs caught before production.
Clear Boundaries — Where Efficiency Gains Go
Time saved is reinvested into

higher test coverage, better documentation, edge-case handling, and code quality layers that prevent costly production issues.

AI at MOCG is not

a shortcut to bill less or deliver faster at the expense of quality. Human oversight, review, and approval remain mandatory.

What This Means for Glia

Your investment delivers more comprehensive quality than traditional development — efficiency gains are reinvested into:

Higher test coverage — 90%+ vs. industry average 70%
Up-to-date documentation maintained continuously
Fewer production bugs — more caught pre-release
Full transparency into how AI is applied in practice