Mercari's pj-double: Agent-Spec Driven Development achieves 150%+ development speed improvement
AI usage at Mercari was fragmented — individual developers used different approaches (Vibe Coding) that could not be reproduced or shared organizationally, widening the gap between AI-proficient and non-proficient developers and preventing company-wide productivity gains.
Early standardization attempts were limited to sharing CLAUDE.md and Cursor Rules locally, without reaching organizational scale. A build trap in QA tooling — delivering a rich UI app instead of minimal prompt infrastructure — raised contribution barriers and slowed method iteration.
SDD-based projects averaged over 150% development speed improvement versus estimates, and over 80% improvement versus other AI-assisted methods.
pj-double expanded from one person to a team of over ten and scaled company-wide across Mercari's full delivery cycle.
Frequently asked questions
What did this team achieve with this AI workflow?
SDD-based projects averaged over 150% development speed improvement versus estimates, and over 80% improvement versus other AI-assisted methods.
What tools did this team use?
Claude Code, DX.
What results were reported?
development speed improvement (SDD vs original estimate): 150%以上; development speed improvement (SDD vs other AI methods): 80%以上; Development efficiency (best-case example): 5分の1; Pj-double team size growth: 1人から10人超のチームに拡大 (source-reported, not independently verified).
What failed first in this deployment?
Early standardization attempts were limited to sharing CLAUDE.md and Cursor Rules locally, without reaching organizational scale.
How is this quality assurance AI workflow structured?
Developer defines task scope → Agent Spec generation → Standards and security validation → Developer clarification → Autonomous implementation → Weekly productivity tracking.