Knowledge graphs for AI agents and enterprise knowledge
We build the backbone of your AI: a clean, queryable graph from your data — source, relationship, meaning. So "chat with your PDFs" turns into a serious platform for agents.
Three problems you can't solve without a graph
Data silos instead of answers
CRM, ticketing, contracts, email — each system knows only a slice. Your users ask across boundaries, the tools can't.
5+ systems per questionHallucinating agents
Vector RAG returns plausible-sounding but ungrounded answers. Multi-hop questions fail reproducibly. You can't build trust on top of that.
~60% multi-hop failureNo auditability
EU AI Act, ISO 42001, SOC 2 demand traceability. "The LLM said so" is not an answer that survives an audit.
Mandatory from 2026What we build
An end-to-end pipeline — from data source to agent-ready query. Hybrid graph + vector, so you get both: reasoning and semantic depth.
A mini graph in action
Click an entity — this is exactly how an agent "thinks" when it answers a multi-hop question.
Mini knowledge graph – click to explore
Pick an entity to see its relationships and attributes. This is exactly how an agent "thinks" when it answers multi-hop questions.
Tip: click a node.
- Branche
- Manufacturing
- MRR
- €12.400
- ←betreutAnna Becker
- →nutztmonday.com CRM
- →unterzeichnetMSA #4471
- →meldetTicket #8821
„What does Acme use, what did they sign, what's broken?"
Two tracks, one stack
Whether you come from the architecture team or you're shipping an agent in production — we meet you where you are.
Enterprise & IT decision makers
When knowledge needs to be made auditable and accessible across system boundaries.
- Ontology workshops with domain experts and IT
- Data residency in the EU, GDPR-compliant architecture
- EU AI Act / ISO 42001 / SOC 2 preparation
- Migration from legacy knowledge systems (SharePoint, Confluence, drives)
- RBAC, audit trails, source attribution on every edge
- Integration with existing IAM, logging and compliance stacks
AI builders & product teams
When your RAG doesn't scale and the next agent should finally do more than chat.
- GraphRAG implementation (LlamaIndex, LangChain, Microsoft GraphRAG)
- Entity extraction pipelines with structured outputs
- Neo4j / Kuzu / Memgraph setup incl. cloud deployment
- Hybrid retrieval: subgraph traversal + vector + reranking
- Agent integration via MCP, LangGraph or custom runtime
- Eval pipelines: multi-hop accuracy, provenance coverage
Our process in four steps
Discovery
Use cases, data sources, success criteria. We bring a ready-made eval matrix, you bring the domain experts.
Ontology & architecture
Which entities, which relationships, which attributes? Which graph DB? Which model for extraction? Result: an executable architecture plan.
Prototype
End-to-end pipeline on a real slice of your data. Incl. hybrid retrieval, agent integration and first eval runs.
Production rollout
CI/CD for extraction, monitoring, incremental updates, re-indexing. Retainer support or clean handover to your team.
Three ways to start with us
Discovery Sprint
We turn "we should do something with knowledge graphs" into an executable architecture plan incl. use-case prioritization, tool recommendation and effort estimate.
- Use-case matrix
- Architecture sketch
- Tool recommendation
- Effort & roadmap
Prototype Build
End-to-end prototype on your real data. Working extraction pipeline, graph DB, hybrid retrieval, agent or API layer. Incl. eval setup.
- Working pipeline
- Graph DB (self-hosted or cloud)
- Agent/API layer
- Eval reports
Production Partnership
We support production: monitoring, re-indexing, new sources, schema evolution, knowledge transfer to your team. Clearly priced retainer slots.
- On-call for pipeline
- Quarterly reviews
- Schema evolution
- Team enablement
Read more on the topic
Frequently asked questions
Do we really need a knowledge graph or is vector RAG enough?
If 90% of your questions are "where does it say this?", vector RAG is fine. Once questions go multi-hop ("which customers of X have Y and Z"), vector breaks down. In the discovery sprint we clarify this with a concrete eval matrix on your real questions.
Which graph DB do you recommend?
Default for enterprise is Neo4j (Aura EU). For embedded and prototypes Kuzu. For streaming use cases Memgraph. We decide during the ontology step based on your real requirements, not benchmarks.
How long until we have a working graph?
A prototype on a real slice of data: 4–6 weeks. A production setup with eval, monitoring and incremental updates: 3–4 months. The discovery sprint gives you a defensible estimate for your case.
How much does it cost?
Discovery Sprint: low five-figure fixed price. Prototype Build: T&M, 30–80k EUR depending on data complexity. Retainer from 3 months with fixed slots. We give you a defensible range after the first call.
Where does our data run?
Default in the EU — Neo4j Aura EU, Frankfurt or Dublin. Self-hosted on-prem or in your own cloud (AWS, Azure, GCP) is possible. LLM calls for extraction can be covered via EU models or on-prem inference (vLLM, llama.cpp).
How do you integrate with our existing stack?
We build the graph as an additional layer, not a replacement. Integration via API, MCP, event streams or classic ETL. RBAC, audit logging and source attribution are part of the architecture from day one.
Can we continue building the graph ourselves later?
Yes, that's the default setup. We build with your team, hand over cleanly documented, and stay available as a retainer for escalations. No lock-in to us as an agency.
Do you have references?
We are currently building knowledge graph setups for mid-market and scale-ups in DACH. We discuss concrete references in the first call — some projects are under NDA, others we are happy to present.
Let's talk about your graph
First call is free, takes 30 minutes and delivers at least three concrete use cases. No pitch deck, no sales call.