Why Naive Vector Search Fails at Enterprise Scale
Simple cosine similarity over basic text chunks fails on complex multi-hop questions, contradictory internal documents, and tabular data. Our Agentic Graph-RAG architecture dynamically reasons across interconnected relational graphs and vector indices.
Hybrid Dense + Sparse Keyword Search
Combines dense vector embeddings with BM25 sparse keyword ranking via Reciprocal Rank Fusion (RRF) for 99.4% context recall.
Self-Correcting Query Planning Agents
Agentic re-writers inspect intermediate search results, verify factual relevance, and reformulate queries dynamically before generating answers.
Document-Level RBAC Access Filtering
Metadata filters enforce user security clearance in real time, preventing unauthorized data leakage across organizational departments.
from llama_index.core import KnowledgeGraphIndex, VectorStoreIndex
from llama_index.vector_stores.milvus import MilvusVectorStore
from llama_index.graph_stores.neo4j import Neo4jGraphStore
# 1. Initialize Hybrid Milvus + Neo4j Graph Storage
vector_store = MilvusVectorStore(uri="http://milvus-k8s:19530", dim=1536)
graph_store = Neo4jGraphStore(username="neo4j", url="bolt://neo4j-cluster:7687")
# 2. Construct Dual Vector-Graph Retriever
graph_index = KnowledgeGraphIndex.from_documents(docs, graph_store=graph_store)
vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
# 3. Agentic Query Engine with Self-Reflection
query_engine = CustomAgenticRAG(
retrievers=[vector_index.as_retriever(similarity_top_k=5),
graph_index.as_retriever(include_text=True)],
reranker="bge-reranker-large",
max_hops=3
)
response = query_engine.query("Analyze Q3 risk exposure across all EU subsidiaries")
Engineering Architecture & Delivery
Neo4j Knowledge Graph Extraction
Automatically parse entity-relationship graphs from unstructured corporate filings, linking suppliers, subsidiaries, contracts, and regulatory constraints.
High-Throughput Milvus / Qdrant Clustering
Deploy distributed HNSW vector clusters on Kubernetes supporting billions of high-dimensional vectors with sub-10ms disk-backed search.
Cross-Encoder Re-Ranking Optimization
Filter out noisy retrieved chunks using BGE-Reranker-Large and Cohere Rerank v3, optimizing LLM context window signal-to-noise ratio.
Strict Verifiable Paragraph Citations
Every claim in the generated output includes interactive deep links directing users directly to exact paragraphs in source PDFs and Confluence docs.
Engineering Architecture & System Implementation Blueprint
Building mission-critical systems requires looking beyond surface-level integrations. We design production platforms engineered from the ground up for deterministic execution, strict compliance boundaries, and ultra-high concurrency. Below is the comprehensive architectural blueprint governing our engineering delivery.
Kernel-Level Concurrency & Compute Acceleration
Modern scale demands bypassing legacy runtime overheads. We leverage low-level primitives including eBPF kernel hooks, lock-free ring buffers, and asynchronous event loops in Rust, C++, and Go to handle hundreds of thousands of concurrent operations per node with sub-millisecond dispatch times.
Zero-Trust Security, VPC Isolation & Governance
Enterprise intelligence cannot compromise on data sovereignty. Every deployment is containerized inside air-gapped Virtual Private Clouds (VPCs) with zero public ingress, strict mutual TLS (mTLS) pod-to-pod encryption via Istio, and hardware-enforced KMS envelope encryption at rest.
Automated Resiliency & Self-Healing Infrastructure
High availability is mathematically proven through chaos engineering drills. We implement active-active multi-region failover protocols with automated DNS routing shifts (Route 53 / Cloudflare) achieving sub-10 second Recovery Point Objectives (RPO) and sub-60 second Recovery Time Objectives (RTO).
Our 5-Stage Execution Protocol
Data Ingestion & Graph Extraction
Parse complex PDFs, Confluence, and databases into semantic node-edge relationships and dense chunk embeddings.
Vector & Graph Cluster Setup
Deploy fault-tolerant Milvus / Qdrant and Neo4j clusters with automated shard replication and HNSW indexing.
Agentic Query Planner Design
Build LangGraph routing pipelines that decompose complex user prompts into multi-step atomic sub-queries.
Ragas & DeepEval Benchmarking
Quantify Faithfulness, Answer Relevance, and Context Precision using 500+ proprietary test questions.
Kubernetes Production Deployment
Deploy containerized RAG microservices behind rate-limited API gateways with streaming SSE responses.
Modern Architecture vs Legacy Approach
See how our cloud-native, sovereign engineering principles outperform traditional development and generic SaaS tooling.
Production Tech Stack & Tooling
Technical Architecture FAQs
How does Graph-RAG outperform standard vector search?
Standard vector search only retrieves chunks that share keyword/semantic similarity. Graph-RAG maps exact structural relationships between entities (e.g. Person X owns Subsidiary Y which signed Contract Z), enabling accurate multi-hop answers.
Can we connect Graph-RAG to internal SQL databases and PDFs?
Yes. Our unified ingestion pipelines automatically index both unstructured formats (PDFs, Notion, Slack, Word) and structured relational SQL databases into the hybrid retrieval engine.
How is sensitive HR and financial data protected?
Every vector and graph node is tagged with granular access control lists (ACLs). When a user queries the system, their OAuth/SAML token automatically filters out documents they do not have permission to view.
What latency can we expect in production?
Our optimized hybrid search and re-ranking pipeline executes in under 65 milliseconds, providing instantaneous response times for enterprise chat and search portals.