Hallucination-free retrieval and agents, grounded in ontologies
Ontologies take retrieval beyond the limits of vector RAG
GraphRAG (graph-based retrieval-augmented generation) is a retrieval architecture that structures entities and relationships with a domain ontology and knowledge graph — instead of searching documents by vector similarity alone — so LLMs reason on traceable evidence. TreeSoop designs ontologies and builds GraphRAG systems, grounded in hands-on experience pushing past vector-search limits across 8 RAG projects, LangGraph orchestration, and VFS-based RAG — rated 4.92/5 on Wishket, Korea's largest dev-outsourcing platform.
Plain vector-similarity search doesn't understand how entities relate — it pulls in irrelevant passages as evidence, and on questions that require multi-hop reasoning it produces hallucinations and inaccurate answers.
Without in-house graph and ontology expertise, teams get as far as a demo, then the project collapses under production-scale data, consistency, and governance requirements. (The leading cause of enterprise GraphRAG failures.)
After the initial build, schemas and entities go stale as the organization changes (drift). With no one accountable for maintenance and improvement, the knowledge graph is abandoned and retrieval quality quietly degrades.
Through workshops with your subject-matter experts, we define the core entities, relationships, and attributes, and design a domain ontology and schema optimized for retrieval and reasoning.
Hybrid retrieval that combines vector search with knowledge-graph traversal finds precise evidence even for relationship-based, multi-hop queries and delivers it to the LLM.
We merge the same real-world entity scattered across documents into one (entity resolution) and model the relationships between entities explicitly — so every answer's provenance can be traced along a graph path.
Semantic-steward automation continuously incorporates schema changes and new entities, and we hand over a governance framework that detects and corrects ontology drift — keeping your knowledge graph alive six months in and beyond.
Together with your domain experts, we define the core entities, relationships, and terminology, and finalize a draft knowledge-graph schema.
We extract entities and relationships from your documents and databases, merge duplicate entities (entity resolution), and build the knowledge graph.
We integrate a GraphRAG pipeline that combines vector search and graph traversal with your LLMs and agents, and verify evidence traceability.
We hand over anti-drift monitoring, semantic-steward automation, and an operations dashboard, transferring a framework for lasting upkeep.
GraphRAG retrieval that structures statutes, internal policies, and contracts as an ontology, returning precise evidence and source paths even for relationship-based queries
Grounding autonomous agents to reason from the knowledge graph rather than vectors alone, preserving factual consistency even across multi-step work
Turning drawings, BOMs, and part specs into a knowledge graph of entities and relationships, enabling relationship-based search for similar parts, substitutes, and related drawings
8
RAG projects delivered
Track record of production vector and hybrid RAG delivery
LangGraph
Orchestration stack
Our standard for multi-step retrieval and reasoning pipelines
1 week
Ontology workshop
Time to define a domain ontology and kick off a PoC
Traceable
Evidence grounding
Every answer traces its sources along a graph path
Our experts will recommend the best approach for your project.
