CASE STUDY // 04•Multi-Agent RAG · Enterprise Search

NexusAgent

An enterprise knowledge platform that connects organizational data sources and generates verified, source-grounded answers with interactive citations.

GitHub Repository

01. Problem Statement & Motivation

Enterprise knowledge is fragmented across GitHub repositories, Jira tickets, Slack channels, Confluence spaces, and internal PDFs. Generic single-shot RAG pipelines retrieve irrelevant context, hallucinate false answers, and fail on domain-specific keyword searches.

WHY THIS MATTERS IN PRODUCTION:Engineers and support teams lose hours hunting for documentation or resolving conflicting system knowledge. Inaccurate AI summaries in enterprise settings lead to costly operational mistakes.

02. System Architecture Design

Employs a multi-agent orchestration workflow in LangGraph. A Query Planning Agent decomposes complex questions into targeted sub-queries. A Hybrid Retrieval Engine searches Qdrant for semantic embeddings and Elasticsearch for exact keyword matches, fusing rankings via Reciprocal Rank Fusion (RRF). A Verification Agent audits retrieved chunks to filter hallucinations before the Synthesizer outputs answers linked to verifiable source citations.

Enforced Reliability & Security Invariants

Strict Document Citations: Every paragraph links directly to clickable source references with chunk highlight coordinates.
Granular Permission Masking: Respects document ACLs so unauthorized users cannot retrieve confidential HR or executive files.
Redis Token & Session Cache: Fast caching for active user dialogues and vector lookup speedups.
Offline Development Bypass: Includes secure mock authentication and fixture data for rapid local developer iteration.

03. Architectural Decisions & Tradeoffs

Reciprocal Rank Fusion (RRF) Hybrid Search

Combines dense semantic vector retrieval (Qdrant) with sparse BM25 keyword search (Elasticsearch). Solves vector blindspots for precise acronyms, commit hashes, and error codes.

Tradeoff: Requires maintaining two search indices and running dual queries in parallel.

Dedicated Factuality Verification Agent

Cross-references draft claims against source chunk text prior to streaming the final response, stripping unsupported assertions.

Tradeoff: Adds ~1.4 seconds to time-to-first-token, but reduces factual hallucination rates to near-zero.

Unified Ingestion Pipeline with Metadata Tracking

Connectors for GitHub, Jira, Slack, and Confluence extract unified metadata (author, timestamp, revision, permissions) with granular chunk-level ACLs.

Tradeoff: Requires custom parsing and tokenization adapters for each SaaS provider API.

04. Verified Empirical Outcomes

Metric Dimension	Baseline	Guarded Platform	Significance
Retrieval Precision @ 5	64.2% (Dense only)	91.8% (Hybrid RRF)	Dramatic improvement on code symbols and product tickets
Hallucination Rate	14.5% (Single-shot)	< 1.0% (Verified)	Verification agent eliminated ungrounded factual assertions
Query Synthesis Latency	p50: 1.8s \| p95: 2.6s	Acceptable enterprise search latency with verified grounding

05. Production Roadmap & Next Iterations

>Implement GraphRAG using Neo4j to map entity relationships across cross-repository code dependencies.
>Add automated daily incremental synchronization via webhooks for Jira and Confluence.

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