Reviews

Honest reads on the tools worth your stack.

Product reviews for architects, platform leads, and the people doing the buying. Verdicts, trade-offs, and the questions a vendor pitch won't answer. Independent — no affiliate links, no sponsored placements.

001

June 1, 2026

Knowledge Graphs & Ontology

11 min

Oracle AI Database 26ai GraphRAG: A Document Graph, Not an Enterprise Ontology

GraphRAG is now a board-level word, and Oracle's version arrives with unusual pull: for many enterprises the data already sits in Oracle, so a few features you already own can turn your documents into a relationship-aware, citable knowledge base with no new platform and no data leaving the building. The proposition is real and, for an Oracle-centric shop chasing trustworthy answers from its documents, it is one of the fastest low-regret moves available. But the word “graph” is doing two different jobs. Oracle 26ai builds a document graph — entities and relationships an LLM extracts from whatever corpus you fed it, shaped by that corpus, excellent for retrieval. It is not a governed enterprise ontology: no shared definition of “Customer,” no reasoning, no constraints, no safe ground for autonomous cross-system agents. The document graph is a property of your documents; an ontology is a property of your enterprise. Buy the first believing it is the second and it fails — not in the demo, but on the third cross-system agent and the first regulatory question it cannot ground.

GraphRAG
RAG
Knowledge Graphs
Ontology Engineering
Vector Databases
RDF / OWL / SPARQL

002

May 30, 2026

Knowledge Graphs & Ontology

12 min

Enterprise Intelligence Platforms — Market Review 2026

The question that decides an enterprise intelligence platform is not which has the best analytics — features change every release — but whether it can serve as a shared foundation every tool and agent connects to, or whether it only governs meaning inside its own walls. Judged that way, on four criteria (semantic scope, external connectivity, access-control architecture, and per-tenant configurability), none of the seven platforms here scores well on all four, and that gap is the finding, not an oversight. Two categories emerge. The ontology-native platforms — Palantir Foundry, Stardog, Graphwise — model typed entity relationships an agent can traverse and, in Stardog's case, infer new facts. The analytics platforms with embedded semantic layers — AtScale, dbt, Cube, Looker — deliver real, immediate metric consistency but govern calculations, not relationships, and each adds another private definition as you add tools. The unmet need across both: no platform ships a pre-built industry ontology you can configure and go live on in weeks, which is where the largest value, for buyers and vendors alike, still sits.

Knowledge Graphs
Ontology Engineering
Semantic Layer
Enterprise Knowledge Graph
Agentic AI
AI Governance