Engineering guides, architecture patterns, and production implementations focused on Vector Search.
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).
How to eliminate enterprise RAG hallucinations: Pairing dense pgvector embeddings with sparse BM25 lexical search, Reciprocal Rank Fusion (RRF), Cross-Encoder reranking, and parent-child document chunking.
The hidden operational tax of running dedicated vector databases alongside your primary database. How PostgreSQL 16, pgvector 0.7+, and HNSW indexing deliver sub-8ms vector search without dual-write synchronization bugs.
Customers do not think in departmental silos: they start on WhatsApp, follow up via email, speak to a rep on the phone, and file an emergency support ticket. Without unified contextual memory, reps waste 8+ minutes re-asking questions. Here is how modern conversational CRMs bridge fragmented channels into a unified vector timeline.
Our engineering team designs sub-50ms distributed backends, edge AI pipelines, and high-concurrency systems.