Generative search introduces failures in query matching, competitive ranking, and grounded generation, while conventional SEO and final mention metrics often cannot locate these failures. We present a controlled Stage-Isolated Evaluation framework that converts stage-specific errors into fact-grounded sitemap revisions. In an industrial study with an anonymized consumer-health company, each version is evaluated against nine fixed competitors using disjoint training, validation, and held-out test queries. The selected checkpoint improves the test score from 58.78 to 69.98, exceeding one-shot rewriting by 6.19 points and same-budget recursion without diagnostics by 3.07 points. Different stages favor different checkpoints, revealing non-monotonic trade-offs. Pilot online results and human annotation provide additional support for the framework as a reproducible diagnostic proxy for enterprise GEO.