NENA is the intelligence behind c:node: a neuro-symbolic, source-based knowledge-graph AI built on open models (open source) that grows continuously with every case. It is built to work traceably instead of guessing — every statement tied to a source, every step verifiable. This article summarizes the peer-reviewed evidence behind it: why pure language models fall short when it matters, why knowledge graphs + rules close the gap, and how this meets the requirements of the EU AI Act.
Large language models form sentences by predicting the statistically most likely next word. The US standards institute NIST notes that fabricated statements (confabulations) are by design in such models — not a slip, but a property of the architecture.1 Looking things up in real documents (retrieval-augmented generation) lowers the error rate markedly but does not remove it: even with the right documents, simple models still add unsupported claims.5 For decisions with consequences, “mostly right” is not enough.
Research calls the approach neuro-symbolic AI: the pattern recognition of neural networks is combined with the verifiability of symbolic rules and knowledge graphs.2 A peer-reviewed survey (NAACL 2024) shows that knowledge graphs as an external knowledge source reduce hallucinations and increase reasoning accuracy.3 And for multi-hop reasoning — connecting several linked facts into one answer — the graph-based variant is demonstrably superior to plain text search.4 This is exactly what NENA is built on.
For high-risk AI, the EU AI Act requires exactly what this architecture delivers anyway: complete logs (Art. 12), transparency and traceable documentation (Art. 13) and the possibility of human oversight (Art. 14).6 Because NENA records every statement with its source and history (provenance), every decision stays verifiable after the fact — without having to expose the model’s internals.
NENA builds on open models (open source) — swappable without changing the traceable logic — runs in the cloud, on your servers or fully offline, and thus stays sovereign in your environment. It is not a static model but a living knowledge graph that grows per tenant with every case. The division of labour: you steer with c:node, NENA thinks, the agents act.
NENA’s direction is guided by academic expertise in decision research and AI.
Research on artificial intelligence, decision processes, machine learning and applied neuroscience — the cognitive and methodological foundations of robust decision intelligence. Selected works:
The sources support the underlying principles (neuro-symbolic AI, knowledge graphs against hallucination, EU AI Act requirements). Advisory-board details are public (Google Scholar); R&D figures will be added after internal clearance.
NENA builds on open-source models — swappable without changing the traceable logic.
GitHubNot a fixed model — NENA grows with you, case by case.
Explore NENAA short test run in your environment — traceable answers, with a source.