[{"data":1,"prerenderedAt":88},["ShallowReactive",2],{"work-enterprise-rag-knowledge-base":3,"work-next-\u002Fwork\u002Fenterprise-rag-knowledge-base":85},{"id":4,"title":5,"accent":6,"body":7,"client":53,"cover":54,"coverAlt":54,"description":55,"duration":56,"extension":57,"featured":58,"industry":59,"meta":60,"navigation":58,"order":61,"path":62,"quote":54,"results":63,"seo":70,"services":71,"stack":75,"stem":82,"year":83,"__hash__":84},"work\u002Fwork\u002Fenterprise-rag-knowledge-base.md","RAG knowledge base for an enterprise services firm","#1A4380",{"type":8,"value":9,"toc":46},"minimark",[10,15,19,23,39,43],[11,12,14],"h2",{"id":13},"challenge","Challenge",[16,17,18],"p",{},"Knowledge lived in SharePoint, email and people's heads. New staff took months to become productive and senior staff were interrupted constantly.",[11,20,22],{"id":21},"what-we-built","What we built",[24,25,26,30,33,36],"ul",{},[27,28,29],"li",{},"Ingestion of policies, playbooks, proposals and templates with permission metadata preserved.",[27,31,32],{},"Hybrid (keyword + vector) retrieval and an LLM answer layer that always cites its sources and refuses when it cannot find one.",[27,34,35],{},"Single sign-on and per-user access filtering, hosted in the client's Azure tenancy.",[27,37,38],{},"Feedback buttons feeding an evaluation set that is re-scored on every content refresh.",[11,40,42],{"id":41},"results","Results",[16,44,45],{},"Internal survey and usage data showed roughly 70% faster resolution of common questions and a sharp drop in interruptions to subject-matter experts. Adoption crossed 60% of staff in the first three months.",{"title":47,"searchDepth":48,"depth":48,"links":49},"",2,[50,51,52],{"id":13,"depth":48,"text":14},{"id":21,"depth":48,"text":22},{"id":41,"depth":48,"text":42},"Professional services firm · 800+ staff (Gulf region)",null,"A retrieval-augmented assistant over thousands of internal documents, with citations and role-based access.","16 weeks","md",true,"Professional Services",{},6,"\u002Fwork\u002Fenterprise-rag-knowledge-base",[64,67],{"value":65,"label":66},"~70%","faster answers to policy and process questions",{"value":68,"label":69},"100%","answers with source citations",{"title":5,"description":55},[72,73,74],"rag-systems","ai-integration","generative-ai-llm",[76,77,78,79,80,81],"OpenAI","LangChain","Milvus","FastAPI","Docker","Azure AD","work\u002Fenterprise-rag-knowledge-base","2024","jRjyRyRFgnkPnJrpeGseskVUH3bTbvj-Jjw8Z18TX_k",{"path":86,"title":87},"\u002Fwork\u002Fcrm-automation-plugin","AI plugin for CRM automation",1792973679704]