A consulting firm called Odra offers a $10,000 one-week engagement where three senior engineers with 30 years combined experience reduce bloated AI-generated codebases. The service targets projects where vibe-coded agents have duplicated logic, created hand-rolled frameworks, and lost architectural coherence past a certain size — typically when adding features breaks unrelated functionality.

Before writing code, the team documents every screen and endpoint into a QA checklist that serves as a safety net. They then consolidate duplicated utilities (fourteen date formatters become one), replace custom frameworks with libraries, and rebuild unsalvageable modules cleanly. The deliverables include the reduced codebase, a CLAUDE.md file, lint rules, and CI checks designed to slow future slop accumulation. A two-week warranty covers any regressions.

Pricing ties payment to results: if the contract promises a 50% reduction and delivers 20%, the client pays $4,000 (40% of the target). Lines are counted by scc (non-blank, non-comment), and code golf is contractually banned. The firm uses Claude Code but keeps the agent on a short leash — experience drives decisions, not the model.

This model exposes a growing operational cost of AI-assisted development: the refactoring tax. Teams adopting coding agents without architectural guardrails accumulate technical debt faster than they ship features. Odra's approach — fixed-scope, outcome-based pricing with measurable reduction targets — could become a template for how organizations manage AI-generated code quality at scale.