Your knowledge base answers are wrong half the time
I build GraphRAG systems that actually understand your domain — connecting articles, products, and processes into a knowledge graph your LLMs can reason over.
Sound familiar?
If any of these hit close to home, you're not alone. This is where most leaders get stuck.
Your internal search returns irrelevant results because it can't understand relationships between concepts
Customer-facing articles are outdated, duplicated, or contradictory — and nobody knows which ones to trust
You tried basic RAG but the answers lack context and miss connections across your knowledge base
Quality auditing tens of thousands of articles manually is impossible — you need automated signals
What is GraphRAG?
GraphRAG combines knowledge graph technology with retrieval-augmented generation to enable LLMs to traverse structured relationships between entities — delivering more accurate, contextually rich answers than traditional vector-based RAG. It excels in enterprise knowledge bases with complex domain ontologies, multi-product portfolios, and cross-referencing requirements.
How we work together
A structured process that reduces risk and gives you visibility at every step.
Ontology Design
Map your domain's entities, relationships, and quality signals into a custom ontology — not a generic template, but one built from your actual content structure.
Graph Construction
Build the knowledge graph in Neo4j with automated ingestion, quality metrics per article (duplication score, relevance, readability), and entity linking.
LLM Agent Layer
Implement multi-hop reasoning agents that traverse the graph to answer complex queries, suggest improvements, and generate new content from verified knowledge.
Visualization & Handoff
Interactive graph visualization for stakeholders, quality dashboards, and full documentation for your team to maintain the system.
What you get
Concrete deliverables, not vague promises.
Knowledge graph connecting tens of thousands of articles with automated quality scoring
Multi-hop reasoning LLM agent answering complex cross-domain queries
Automated quality metrics: duplication detection, relevance scoring, readability index, translation gap analysis
Interactive knowledge graph visualization with multi-dimensional filtering
Proof it works
Real projects where this approach delivered results.
Knowledge Graph for Global HealthTech Product KB
Global HealthTech Company
14,800+ nodes and 24,900+ relationships mapped in the knowledge graph
Technologies I work with
Typical investment
Depends on scope, timeline, and complexity. Let's discuss your specific situation.
EUR 20,000 - 60,000
per project
Common questions
Ready to fix your knowledge base?
Let's have a 30-minute conversation about your challenge. No pitch, no pressure — just an honest assessment of whether this is the right approach for you.
Let's Talk on LinkedIn