Agentic RAG & Enterprise Knowledge Graphs
Eliminate model hallucinations and context window constraints with sovereign, self-correcting Agentic RAG graphs. We engineer hybrid dense-sparse vector pipelines backed by Milvus 2.4 GPU acceleration, Cohere ReRank 3, and sub-45ms query SLAs.
Beyond Naive Vector Search to Autonomous Self-Correction
Standard top-k vector retrieval suffers from context window pollution and single-pass query ambiguity. Our Agentic RAG state graphs actively evaluate retrieval quality, decompose complex prompts, and execute dynamic fallback nodes.
LangGraph State Machine Topology
Multi-node state graphs evaluate retrieval confidence scores, routing to query expansion or web fallback automatically.
Milvus 2.4 GPU-Accelerated Search
Sub-45ms HNSW and CAGRA GPU vector indexes scaling to billions of dense 1536-dim embeddings.
3-Tier Hybrid Reciprocal Rank Fusion
Merges sparse BM25 token precision with dense semantic vectors, refined via Cohere ReRank 3 cross-encoders.
# Production Agentic RAG State Machine
from langgraph.graph import StateGraph, END
from pymilvus import Collection
class RAGState(dict):
query: str
documents: list
critique_score: float
def evaluate_retrieval(state: RAGState):
if state.get("critique_score", 0.0) < 0.85:
return "query_expansion_node"
return "synthesis_node"
workflow = StateGraph(RAGState)
workflow.add_node("retriever", execute_hybrid_search)
workflow.add_node("evaluator", evaluate_retrieval)
workflow.set_entry_point("retriever")
Enterprise RAG & Knowledge Graph Delivery
Dense-Sparse Hybrid Vector Search
Combines BM25 exact keyword matching with 1536-dim dense vectors for zero-precision-loss retrieval.
100% Data Sovereignty & VPC Security
Deploys on-premise or within isolated VPC perimeters with zero third-party data telemetry.
Knowledge Graph Entity Extraction
Extracts structured entity relationships and schemas for deterministic multi-hop reasoning.
GPU-Accelerated CAGRA Indexing
Parallel CUDA graph traversal delivering 1,450+ QPS search throughput.
Agentic RAG System Architecture & Implementation Blueprint
We engineer production RAG platforms built from ground zero for deterministic precision, strict compliance boundaries, and sub-45ms execution SLAs.
Stateful Graph Orchestration
LangGraph state channels track intermediate reasoning, executing dynamic query re-writing loops when confidence is low.
Air-Gapped VPC Security & RBAC
Document-level role-based access control (RBAC) ensures users only retrieve context matching their permission token.
PostgresSaver Thread Persistence
Every retrieval state transition is check-pointed in PostgreSQL for instant crash recovery and audit trails.
Our 5-Stage Agentic RAG Protocol
Data Ingestion & Chunking
Hierarchy-aware semantic chunking and metadata enrichment across PDFs, SQL, and enterprise wikis.
GPU Vector Indexing
Milvus 2.4 CAGRA/HNSW GPU index building with dual dense-sparse embeddings.
State Graph Engineering
Designing LangGraph decision nodes, evaluation functions, and cross-encoder re-ranking stages.
Benchmark Stress Testing
Evaluating 10,000+ synthetic multi-hop prompts to measure accuracy and P95 latency.
Production Cluster Deployment
Containerized Kubernetes deployment with active-active pod auto-scaling.
Agentic RAG vs Legacy Naive Search
Compare how sovereign Agentic RAG state graphs outperform single-shot vector lookups and generic SaaS search.
Technical Architecture FAQs
Key architectural, integration, and scalability details for enterprise engineering teams.
Naive RAG relies solely on single-shot top-k cosine similarity, which fails on complex multi-hop queries and pollutes context windows. Agentic RAG uses state machines to decompose prompts, evaluate context precision, and trigger fallback search nodes dynamically.
Yes. We deploy 100% sovereign Agentic RAG stacks on your bare-metal GPU clusters or isolated VPC using Milvus 2.4, local vLLM model instances, and Cohere/BGE cross-encoders.
We enforce document-level scalar metadata filtering inside Milvus and PostgreSQL queries, ensuring retrieved context matches the authenticated user's access token.