Hallucination
Unsourced answers assert things with nothing to back them.
ONTOVIA
Knowledge-graph AI: fewer hallucinations, every answer sourced
Hybrid
Vector search + knowledge graph
Grade 1
GS Certification (Korea)
2025
Released
Air-gapped
On-premise deployment
An enterprise hybrid RAG platform built on ontology and knowledge graphs
Patented GraphRAG combines with vector search to turn scattered enterprise data
into knowledge. Every answer leaves an evidence path you can verify inside an air-gapped network.
What ordinary RAG misses
Vector search alone struggles to meet requirements for relationships,
evidence, and air-gapped operation.
Hybrid retrieval
Combines vector similarity search
with knowledge-graph reasoning for higher accuracy.
Multi-LLM question answering
Choose or run online and offline LLMs
side by side, per network policy.
Sourced answers
Every answer shows its evidence path, and the source document is one click away.
Reasoning visualization
A network of concepts (nodes) and relationships (edges)
shows the reasoning step by step.
Unified management dashboard
Embedding, reranking, models, prompts, and guardrails
managed on one screen.
MCP data integration
Connects through
the MCP (Model Context Protocol) standard.
How it works —
the RAG pipeline
Scattered data becomes knowledge, then flows through retrieval, reasoning, and sourced answers in one pass.
Data
Structured and unstructured ingestion
Knowledge structuring
Entity and relationship extraction ·
ontology mapping
Embedding
Text to vectors
Vector DB + knowledge graph
Similarity + relationship indexing
Reranking
Relevance scoring
Multi-LLM
Question answering
Sourced answers
Source · direct link
How this differs from ordinary RAG
Add a knowledge graph and accuracy, sourcing, integration, and operations all change.
CategoryOrdinary Vector RAGONTOVIA
Retrieval methodVector similarity onlyVector similarity + knowledge-graph relationship reasoning
Hallucination and accuracyContext drift, frequent hallucinationRelationship and source verification reduce hallucination
Source tracingUnclear, chunk by chunkClear sourcing via graph paths
Data integrationMostly documentsStructured (DB) + unstructured, integrated
Operating environmentDependent on external LLMsOnline and offline multi-LLM · air-gapped
Knowledge AI,
proven in production
Delivered and in use across public-sector and enterprise sites.
Korean national software
quality certification
10
Registered patents
(knowledge graph, RAG core technology)
2025
General availability, v1.0
Frequently asked questions
Do we need to train (fine-tune) a model on our own documents?
No fine-tuning required.
Internal documents are structured into a knowledge graph and connected through hybrid RAG, so answers rest on the data your company already holds.
Internal documents are structured into a knowledge graph and connected through hybrid RAG, so answers rest on the data your company already holds.
How is this different from a chatbot or plain vector RAG?
It combines vector similarity search with knowledge-graph reasoning. Relationships and context are not lost, and every answer carries a source path, which reduces hallucination.
Does it make up answers with no evidence?
Every answer comes with the source document and the evidence path. The original is
one click away, which keeps hallucination to a minimum.
one click away, which keeps hallucination to a minimum.
Can it run air-gapped, with no data leaving?
In an air-gapped (on-premise) configuration, it can run entirely on offline LLMs, which shrinks the boundary where data crosses outside. Running an external LLM alongside it is optional.
Can it search structured databases and scattered documents together?
MCP data integration connects structured databases and unstructured documents through a standard interface, searchable in a single query.
What do we need in place to adopt it?
It ships and installs with a recommended GPU server configuration, and we support data connection and ontology design. We'll recommend the right setup after a consultation. Integration with your existing document access control system and the scope of data handling are designed together during the adoption consultation.
Evaluating knowledge AI?