AI Agents · GraphRAG

Hallucination-free AI agents, grounded in a knowledge graph.

In an era where LLM outputs can't be taken at face value, every answer is verified against real graph queries and returned with source nodes.

Trustworthy · Traceable · Sovereign

Limitations of today's LLMs

Three reasons enterprises can't yet trust LLMs.

Hallucination

LLMs produce plausible-sounding but incorrect answers. In enterprise contexts, this is unacceptable.

No verifiability

There is no way to trace why a particular answer was given — making adoption difficult in audit-heavy or regulated environments.

Vendor lock-in

Relying on a single LLM API exposes organizations to cost volatility, policy changes, and data sovereignty risks.

Our approach

Verifiable agents built on a knowledge graph.

GraphRAG

Graph-augmented Retrieval

Unlike vector search, GraphRAG traces entity relationships directly within the knowledge graph — delivering accurate, context-grounded answers without hallucination.

Vector RAG — document similarity matching
GraphRAG — direct entity relationship traversal
Every answer verifiable via Cypher query

Agent-to-Agent (A2A)

Multi-agent Orchestration

Specialized agents collaborate to execute complex workflows — analysis, reporting, and alerts all within a single orchestrated pipeline.

Specialist agent pool (analysis · reporting · search)
Workflow orchestration
Built-in human-in-the-loop (HITL) checkpoints

MCP · Standard Integration

Model Context Protocol

OntoCraft's knowledge graph is accessible directly from any MCP-compatible client, including Claude Desktop and Cursor — with no proprietary connectors required.

Graph exposed as an MCP server
Compatible with any MCP-compliant LLM client
Vendor-neutral standard interface

Local LLM Integration

On-prem Inference

Ollama · vLLM · LM Studio. Full agent capability in air-gapped environments. Your data never leaves your infrastructure.

Open models: Llama · Gemma · Qwen and more
On-premises GPU or CPU inference
Zero API cost — only infrastructure overhead

How it differs from traditional RAG

Relationship traversal, not vector matching.

GraphRAG verifies every answer with the executed query and source nodes — the structural reason hallucination disappears.

How it works

From query to verified answer, in 4 steps.

Step 01

User query

Natural language input

Step 02

Agent routing

Specialist agent selected

Step 03

Graph traversal

Cypher query executed

Step 04

Verify + respond

Returned with source nodes

Every answer is returned with the executed Cypher query and the supporting source nodes

Experience hallucination-free AI agents firsthand.

Request a demo→ONTOFLOW Platform