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We are an AI research and consultancy company from Frankfurt, Germany, and the team behind c:node.
Our work starts from one question: how do you build AI that has to justify its answers? Public administrations, banks and the German Mittelstand cannot act on "the model said so". Every statement needs a source someone can check, every decision a record, and nothing should leave the system without a person approving it.
| Answers grounded in a continuously growing knowledge graph of EU and German law, public registers and market data — with the norm, register entry or document each statement rests on. | Agents propose, people decide. Mails, tasks and changes in connected systems stay drafts until someone approves them, and every decision lands in an audit trail. | EU hosting, GDPR and the EU AI Act built in, pseudonymisation before any external model call, and an on-premises edition down to air-gapped setups on open-weights models. |
flowchart LR
Q["Question"] --> R{"Retrieve"}
G[("c:node Graph<br/>law · registers · tenders · market")] --> R
D[("Your organisation's<br/>documents & systems")] --> R
R --> A["Answer<br/>with a source per statement"]
A --> H{"Action with<br/>external effect?"}
H -- "no" --> U["Answer to the user"]
H -- "yes" --> P["Proposal card"] --> AP["Human approval"] --> X["Executed + logged"]
| 52,000+ concepts · 106,000+ relations | EU and German law down to the article, case law, authority decisions, ontologies (EuroVoc, NACE, ELI/CELEX) |
| 3.1 million+ company records · 51,000+ public tenders | public registers and procurement notices, refreshed by a daily ingest |
| 99.9%+ of concepts anchored to an official source | measured on the graph itself, before any question is asked |
| 33 domains | from finance and public administration to health and energy |
Figures as of November 2026; live counts in the public observatory at graph.c-node.ai.
We publish methods, not implementations: how we evaluate, govern and grow systems that answer with evidence, mapped to public standards so others can apply and check them.
| Publication | What it is | Cite |
|---|---|---|
| grounded-ai-evals | A four-layer method for evaluating AI systems that answer with evidence — graph correctness, ontology hygiene, answer groundedness, baseline comparison — plus a release gate that blocks on named checks instead of a weighted mean. |  |
| governed-graph-growth | A technical note on keeping a continuously ingested, provenance-first knowledge graph trustworthy as it grows: source authority tiers, identity before enrichment, rule-classed linking, schema re-validation, growth health signals. |  |
- Pilots — public administrations, financial institutions and mid-sized companies that want AI they can audit: start at c-node.ai.
- Research — universities and research institutes working on grounded AI, legal knowledge graphs or human oversight of AI agents: we are open to joint work and co-authored publications.
- Contact — contact@creativate.tech · Leonardo Bornhäußer, CTO
Trust is good. Evidence is better.