Standard vector search retrieves documents based on keyword similarity—which fails when answers require multi-hop reasoning. We engineer intent-aware retrieval engines backed by Neo4j, allowing your AI to autonomously navigate relationships to surface exact answers.
If an analyst asks, "Which logistics contracts are exposed to the new tariff?" or a student asks, "Why do I use 'ser' instead of 'estar' here?", a vector database simply returns paragraphs containing those words.
Graph-backed RAG is different. Our Planner Agents write dynamic queries to traverse the graph, connecting contracts to suppliers, or native grammar to target concepts, delivering precise, explainable context.
from langchain_community.graphs import Neo4jGraph
# Connecting to the unified Enterprise Knowledge Graph
graph = Neo4jGraph(url="neo4j+s://enterprise-prod.db")
def agentic_graph_retrieval(intent_type: str, params: dict) -> dict:
"""
The LLM dynamically selects the traversal path based on the domain.
"""
if intent_type == "B2B_SUPPLY_CHAIN":
# Traverses: Supplier -> Component -> Risk_Factor
cypher = """
MATCH (s:Supplier {id: $supplier_id})-[:PROVIDES]->(c:Component)
MATCH (c)-[:AFFECTED_BY]->(r:Risk_Factor {type: $risk_type})
RETURN s.name, c.part_number, r.severity
"""
elif intent_type == "LANGCASE_PEDAGOGY":
# Traverses: Mastered Concept (Native) -> Analogous Concept (Target)
cypher = """
MATCH (s:Student {id: $student_id})-[:MASTERED]->(known:Concept)
MATCH (known)-[:ANALOGOUS_TO]->(target:Concept {name: $target_concept})
RETURN known.name AS bridge_concept, target.rules AS target_rules
"""
return graph.query(cypher, params=params)In our architecture, the LLM is a database operator. When a user asks a complex question, our Planner Agents analyze the graph schema, write the exact Neo4j Cypher query required, execute it, and verify the results.
Whether auditing a global supply chain or teaching a student native-to-target grammar analogies, the retrieval logic remains structurally robust and mathematically sound.
Navigates deep data relationships beyond single keywords.
Retrieves bridging concepts with clear node-level lineage.
We evaluate retrieval methods based on determinism and multi-hop reasoning. Here is why we deploy Graph-backed RAG for complex enterprise and educational platforms.
Date: 2026-05-09 · Status: Standardized
| Retrieval Method | Multi-Hop Reasoning | Explainability | Hallucination Risk |
|---|---|---|---|
| Graph RAG (Neo4j) | Excellent | High (Exact Nodes) | Low |
| Standard Vector RAG | Poor | Low | High Risk |
Core retrieval systems will utilize Neo4j Knowledge Graphs. This is mandatory for domains requiring high accuracy (B2B Supply Chain, Compliance, EdTech) where structural relationships matter more than semantic keyword matches.
If your current AI hallucinates because it can't connect the dots in your data, you don't need a better prompt—you need a better architecture.