We present evidence that the Knowledge Retrieval Paradox — where naive RAG degrades rather than improves specialist clinical AI performance — is not intrinsic to retrieval-augmented generation but an artefact of retrieval imprecision in specialist domains. Our ORVS-QS system combines structured 4-dimension verification (Clinical Accuracy 0.30, Safety 0.30, Therapeutic Management 0.20, Resource Stewardship 0.20) with corpus-curated PCA quantisation of 81,502 rheumatology article embeddings (335 MB compressed to 39 MB, 95% recall at 10). Across 125 clinical scenarios in 7 protocols, Full-ORVS+QS achieved 8.90 composite vs 8.18 vanilla GPT-4o (+8.8%), reduced hallucination from 12-15% to under 2% (6-fold reduction), lowered inter-scenario variance by 89%, and improved safety scores by 7.3 points. Bayesian posterior probability of superiority: 0.89 (95% CI 0.82-0.94). The Knowledge Retrieval Paradox is resolved: generic TurboQuant achieved only 87% recall because random rotations destroy the anisotropic structure of specialist medical embeddings. Corpus-curated PCA preserves it. Services priced via x402 micropayments on Base L2: single verification $0.50, full ORVS pipeline $2.00, QS retrieval query $0.25, TRUST-Bench evaluation $1.00 USDC.