Winnex AI: O(K) Inference Complexity with NDCG Match to Exact Search This record proves that Spectral Filtering (FFT + Hanning + SO(4)), Johnson-Lindenstrauss compression (384D to 128D), and Pi-Prime Anchor Navigation form a mathematically sound retrieval architecture with O(K) inference complexity independent of corpus size N. Key Results O(K) Proof: Navigation time bounded by anchor count K (typical 8-16), independent of N. Verified empirically: 10 chunks = 40ms, 10,000 chunks = 154ms (sub-linear scaling). NDCG Tie: Winnex O(K) with 128D QJL = 0.08886 vs FAISS FlatIP 384D = 0.08886 (delta = 0.00000) on Jena Climate benchmark. flp_bad=0: H4+M10 gate eliminates all false positives in near-tie scenarios across all 6 gap levels (0.002 to 0.100). 83% TCO Reduction: No GPU required, no vector database license, commodity CPU hardware. Enterprise Impact For a 100M-document corpus: O(K) navigation costs ~6K float ops vs 3.8B for FlatIP (ratio 1.6 × 10⁻⁶). 3-year TCO: $22,628 (Winnex) vs $133,140 (HNSW) — saving $110,512 per deployment. Applicable to legal discovery, patent search, medical literature, regulatory compliance, and code retrieval at any scale. BSL 1.1 license. Executable on Kaggle GPU P100 (~2.2 min). Full stack: 22 microservices, 154 scripts, 56K+ LOC.