Engineering guides, architecture patterns, and production implementations focused on Rag.
Scale semantic RAG to 10M+ embeddings: HNSW graph skip-list mathematics, pgvector 0.7 memory bottlenecks, Qdrant 8-bit scalar quantization, and hardware SIMD acceleration.
Eliminate silent retrieval degradation in enterprise RAG pipelines: Mean Reciprocal Rank (MRR), Hit Rate @ K, nDCG evaluation, automated synthetic query generation with LLM critique filters, and CI/CD quality gates.
Eliminate hallucination and retrieval failure in financial RAG pipelines: multi-level header linearization, deterministic footnote graph binding, dual-representation indexing, and dense-sparse Reciprocal Rank Fusion (RRF).
Our engineering team designs sub-50ms distributed backends, edge AI pipelines, and high-concurrency systems.