// disciplined_vibe_coding
AI-Assisted Development, Done Right
The vibe coding revolution is real - but production-ready AI development requires engineering discipline. We help teams capture the speed without sacrificing quality, security, or architectural integrity.
// what_we_offer
Vibe Coding Services
Three specialised offerings built from real production experience with AI-assisted development.
Prompt Engineering Training
Teaching teams how to communicate effectively with AI development tools - prompt structure, screenshot-based debugging, context management, and knowing when to prompt vs. code manually.
API Layer Architecture
Designing and building the API middleware that AI tools can consume. In the age of regenerable code, whoever controls the API layer controls the product.
Security Audit for AI-Built Projects
Professional review of AI-generated codebases before production. We catch missing auth, exposed secrets, and overly permissive CORS that non-technical builders can't assess.
"It's not about writing code anymore - it's about seeing what needs to change. In the age of AI-generated software, the engineer's value shifts from typing to judgment. You need domain expertise to spot what the AI got wrong, because it won't tell you."
- Aby Varghese, CTO
// tools_we_build_with
Our Tooling
We don't just talk about AI coding - we ship production software with it daily. These are the platforms powering our disciplined vibe coding workflow.
Claude Code
Anthropic
Agentic coding with deep contextual understanding. Claude Code navigates entire codebases, reasons about architecture, and executes multi-file changes - while we apply the engineering judgment that keeps production systems reliable.
Deep context • Agentic workflows • Architectural reasoning
Codex
OpenAI
Autonomous code generation, refactoring, and task execution. Codex handles the heavy lifting of boilerplate and transformation - while our engineers validate every output against real-world requirements.
Code generation • Refactoring • Autonomous execution
// methodology
Key Principles
Drawn from months of production experience building with AI coding assistants.
Testing-First, Every Iteration
Test after every single prompt cycle. AI can fix one component while silently breaking three others.
Screenshot-Based Prompting
Annotated screenshots reduce UI fix cycles from 3-4 prompts down to 1-2. Show the AI what's wrong, don't describe it.
Diff Debugging Over Fix Spirals
When something breaks, read the Git diff first. The ground truth is in what actually changed, not what the AI thinks it changed.
// from_the_field
From the Field
Real use cases from our MCP and AI integration work - anonymised, but drawn directly from production engagements.
AI-Predicted Workflow Nodes
A flow builder with hundreds of historical workflows. We added an MCP layer so the AI could analyse patterns and predict the next node - generating entire workflows from plain English descriptions.
Outcome:
Users went from manual drag-and-drop to AI-generated workflows reflecting their organisation's specific conventions.
Intelligent Template Generation
A static template library transformed through MCP. The AI accessed historical template performance data to generate strategically optimal templates - not just linguistically correct ones.
Outcome:
Templates now reflect formatting, language patterns, and timing that historically drive higher engagement.
Data-Driven Campaign Strategy
Years of campaign performance data, made accessible to AI through MCP. The system recommends optimal send times, channel mix, and messaging approaches based on historical analysis.
Outcome:
Marketing teams shifted from gut-feel to data-informed decisions without needing data science tools.
// deep_dives
Further Reading
Article - 10 min read
Vibe Coding Done Right: Why AI-Assisted Development Still Needs Engineering Discipline
The complete methodology - testing-first rules, diff debugging, screenshot prompting, and the domain expertise problem.
Article - 12 min read
MCP Servers: The Missing Layer Between Your Software and AI
How MCP makes every enterprise product AI-powered without a rewrite - with real use cases from production engagements.
Ready to Build with AI - Properly?
Whether you need training, architecture review, or a full MCP integration - your first conversation is with an engineer, not a salesperson.