Vector-Only Isn’t Enough: Azure AI Search + Semantic Ranker for Real RAG | Mudassir – Cognilium AI

Description

Vector-only RAG breaks in production. Cognilium AI (led by Mudassir Marwat) builds hybrid retrieval on Azure AI Search + Semantic Ranker for accurate, governed, cost-efficient answers at scale. This is a proof-of-build: not theory, not a lab demo. We show the architecture and operating model we use to ship real-world RAG—without a standalone vector DB—that holds up under heterogeneous corpora, live updates, and governance. WHAT’S INSIDE • Hybrid retrieval: keyword + vector + semantic ranking (precision that sticks) • Semantic re-ranker + BM25: filters, facets, synonyms; fielded search signals • Index everything: PDFs, websites, blobs, OCR, enrichment; freshness maintained • Governance-native: RBAC, ACLs, field-level controls, audit trails (no bolt-on middleware) • Query pipeline: rewrite → hybrid retrieve → semantic re-rank → ground → citations by default • Ops impact: better recall, fewer retries, stabilized P95 latency, lower cost-per-answer WHY IT MATTERS Vector-only fits narrow prototypes. Azure AI Search + Semantic Ranker delivers accuracy, governance, and unit-economics you can run in production—built once, scales everywhere. ABOUT COGNILIUM AI We build production-grade GenAI systems: Hybrid/Vector-free RAG, NL2SQL assistants, agentic workflows, voice AI pipelines—engineered for reliability, observability, and measurable ROI. Founder: Mudassir Marwat — builder-first, production-first. CHAPTERS 00:00 Vector-only RAG: why it fails in production 00:24 Hybrid retrieval on Azure AI Search (overview) 00:49 Semantic re-ranking, BM25, filters & facets 01:09 Governance: RBAC, ACLs, audit, field controls 01:30 Cost & latency: retries ↓, P95 stable, token spend ↓ 01:55 Build once. Scale everywhere. Cognilium AI #CogniliumAI #MudassirMarwat #AzureAISearch #HybridRAG #SemanticRanker #VectorFreeRAG #EnterpriseAI #GenAI #AIEngineering #ProductionAI