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.
- 001June 1, 2026Knowledge Graphs & Ontology11 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
- 002May 30, 2026Knowledge Graphs & Ontology12 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
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