Our Workflow: How Idin Studio Integrates AI in Every Sprint

Not just "using AI occasionally"

When people say "we use AI in development," they often mean: the developer uses AI autocomplete while writing regular code. This is valid, but not what we mean.

At Idin Studio, AI is integrated into **every phase of the sprint** — not as a shortcut, but as a multiplier for quality and speed.

Phase 1: Discovery & Architecture

Before writing a single line of code, we use AI for:

**Brainstorming architecture**: "We need a multi-tenant system with per-usage billing, these are the technical constraints — what are the trade-offs between approach A vs B?" AI is excellent for exploring possibilities and identifying potential issues that might be missed.

**Generate initial ERD**: Based on requirements, AI can draft an entity-relationship diagram in minutes. This isn't final — it's a starting point for team discussion.

**Technical spec review**: Paste the spec document into AI and ask "what's ambiguous, what could be a problem in implementation?" The results are always surprising — AI finds edge cases that were missed.

Phase 2: Setup & Scaffolding

This is where AI saves time most directly:

• Setup project boilerplate with the right configurations (TypeScript strict mode, ESLint, testing setup)
• Generate database schema from ERD
• Create folder and file structures according to the agreed architecture
• Setup basic CI/CD pipeline

Work that previously took the first half of a sprint day can now be completed in 1-2 hours — and with more consistent quality because we have templates and context provided to the AI.

Phase 3: Feature Development (Core Sprint)

This is what's most different from traditional workflows:

**Context file**: We maintain a file describing the project architecture, code conventions, patterns used, and important technical decisions. This file is always loaded into the AI agent at the start of every session. Result: AI produces code consistent with the entire codebase, not "generic" code.

**Task decomposition**: Every large feature is broken down into specific tasks that AI can work on in one session. Not "implement authentication" (too broad), but "implement JWT token generation with refresh token rotation using Redis for storage."

**Pair programming with AI**: Developers don't leave AI to work alone. The developer reviews every batch of generated code, gives feedback, and steers in the right direction. This is more like a senior developer mentoring a very fast-learning junior.

Phase 4: Review & Quality Assurance

**Automated code review**: Before human code review, we run an AI code review that checks for: security vulnerabilities, performance issues, code inconsistent with the codebase, and potential bugs.

**Test generation**: From the written code, AI generates a comprehensive test suite. The developer reviews test cases to ensure truly important scenarios are covered.

**Documentation**: Docstrings, README updates, and changelogs — AI handles these automatically based on code changes.

Results in Numbers

We don't have a formal study, but based on developer estimates:

• Features that previously took 3 days: now 1-1.5 days
• Bug fixing that previously took 4 hours: now 1-2 hours
• Setup new project: from half a day to 2 hours

More importantly: the "saved" time isn't lost — it's allocated to things that require human judgment: architecture decisions, user feedback analysis, and product thinking.

What AI Cannot Do

For balance, here's what still needs humans:

• **Business judgment calls**: Is this feature worth building? Which tradeoff is acceptable?
• **Deep security audit**: AI is good at common vulnerabilities, but novel attack vectors need human expertise.
• **UX intuition**: Does this flow feel right to a real user?
• **Stakeholder communication**: Explaining technical decisions to non-technical clients.

This workflow isn't about reducing the developer's role — it's about the developer focusing on what's most valuable.