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 delivery accelerator, not a decision-maker.
We embed AI across discovery, engineering, and quality workflows to improve consistency and scalability — and to reinvest saved time into higher quality — while ownership, accountability, and release authority remain fully human-owned.
This approach allows us to scale responsibly — without compromising trust, transparency, or delivery discipline.
Accelerated Understanding (Discovery & Design)
- AI helps teams synthesize large volumes of input early, enabling faster alignment and clearer direction.
- Tools include Firefly (structured insight extraction), GitHub SpecKit (early technical drafts), and NotebookLM (living knowledge repositories).
- Outcome: clearer scope earlier to reduce rework later.
Engineering Acceleration (Development)
- AI acts as a pair-programming and knowledge acceleration layer, reducing friction in day-to-day engineering work while preserving code quality.
- We leverage tools such as Cursor, GitHub Copilot, Claude, Grammarly, and Glia AI / Knowledge Base AI to support coding, reviews, documentation, and platform knowledge access.
- All architecture decisions, code reviews, and production readiness remain human-led.
- Outcome: less low-value toil, more time for reviews, hardening, and maintainability.
Expanded Confidence (Testing & Quality Engineering)
- AI enables broader scenario coverage and faster feedback, particularly for conversational and complex systems.
- Our quality stack includes LLM-based Sim Users & Judges, OpenAI cloud services for similarity scoring, Dockerized local LLMs for controlled role-play testing, Langfuse for observability, Playwright (MCP) for deterministic testing, and Promptfoo & Giskard for AI quality and security validation.
- Final QA approval and release decisions are always manual.
- Outcome: more time spent on coverage of critical paths, edge cases, and regression prevention.
- More tests and stronger coverage on critical user flows
- Deeper reviews and better alignment with patterns/standards
- Better documentation: ADRs, runbooks, and clearer “how to verify” steps
- Fewer regressions and “surprises” in user-facing behavior
- Faster debugging and clearer handoffs
- More stable releases and improved maintainability over time
a force multiplier, a consistency enhancer, and a way to scale delivery responsibly.
a replacement for engineers, a decision-maker for scope or estimates, or a shortcut that reduces accountability.
A delivery model that combines engineering ownership with AI acceleration, resulting in: