Research, guides, and perspectives from the AdeptNova team — helping enterprise leaders build AI they can trust, explain, and audit.
Gartner reports 87% of AI projects never reach production. The industry blames data quality, talent, and tooling. But the real culprit is structural: AI systems lack a semantic foundation — a shared, machine-readable understanding of what enterprise data means. Without it, models hallucinate, can't be audited, and fail across domains. Here's how ontology and knowledge graphs close the gap.
The three-layer semantic stack: Ontology → Knowledge Graph → Contextual AI. This article explains why each layer is essential and how they work together.
EU DORA is live. Penalties reach €20M or 10% of turnover. This guide explains what financial institutions must do — and how a semantic approach maps requirements to controls automatically.
Scope 3 emissions live in your suppliers' suppliers. Manual tracing takes months. Here's how knowledge graphs automate multi-tier supply chain carbon traceability — with full audit trails.
Standard RAG retrieves similar text chunks. GraphRAG traverses knowledge graphs for multi-hop, relationship-aware reasoning. We compare accuracy, auditability, and hallucination rates head-to-head.
Organised fraud operates as networks — shared providers, recycled addresses, coordinated timing. Flat data models can't see them. Here's how graph-based intelligence catches what traditional systems miss.
LLMs are fluent but not truthful. In regulated industries, you need repeatable, verifiable, auditable AI. Neural-symbolic architectures combine language with logic — here's how.
FATF estimates TBML accounts for $1.6T annually, yet less than 5% of shipments are checked. Enterprise solutions cost £500K+. Here's how mid-market banks can deploy sophisticated TBML intelligence at a fraction of the cost.
No spam. Just expert thinking on knowledge graphs, enterprise AI governance, and building trustworthy AI — once a month.