Writing & Monographs

Featured Monograph • Part 1 of 6 • November 2026 • 13 min read

Why Enterprise AI Hits a Wall: The Limits of RAG, Long Context, and the Case for Knowledge Graphs

Teams across the industry are discovering that bolting an LLM onto a vector database or a 1M-token context window produces impressive demos that stall in production. This opening monograph audits Vector RAG, Long Context, Relational SQL, and proprietary GraphRAG—and shows why open-standards Knowledge Graphs are the missing deterministic spine for enterprise AI.

Read Full Monograph →

4 Stacks Vector RAG, Long Context, SQL & GraphRAG Audited

90%+ Lower Context Token Footprint via Deterministic Subgraphs

Part 1 of 6 Knowledge Graphs for the AI Era Series

Semantic AI & RDF • Part 1 of 6 • 13 min read

Why Enterprise AI Hits a Wall: The Limits of RAG, Long Context, and the Case for Knowledge Graphs

Why cosine similarity, giant prompt windows, rigid SQL schemas, and LLM-extracted property graphs break on enterprise business logic and unit economics—and how open Knowledge Graphs solve both.

Semantic AI & RDF • Part 2 of 6 • 14 min read

Things, Not Strings: How Knowledge Graphs Actually Model Your Business

A plain-English introduction to global identifiers (URIs), Subject-Predicate-Object triples, and why elevating relationships to first-class citizens lets your data model evolve without schema migrations.

Web & Systems Engineering • Part 3 of 6 • 16 min read

From Everyday JSON to a Knowledge Graph: Why Getting Started Is Easier Than You Think

How translating public organizational facts into plain JSON-LD files on disk (@context, @id, @type) built the 4,850-triple Evidence Action Knowledge Graph in two weeks—and let a third-party hiring feed snap in without vendor support.

Semantic AI & RDF • Part 4 of 6 • Coming Soon

Breaking Down Silos: Shared Vocabularies, Taxonomies, and Connecting to Global Knowledge

Using open vocabularies (Schema.org, SKOS) and external graphs (Wikidata) so Engineering, Product, and Growth share one unambiguous definition of customers, capabilities, and markets.

Semantic AI & RDF • Part 5 of 6 • Coming Soon

Deterministic Logic vs. Probabilistic Guessing: How Inference Solves What LLMs Can't

Why logical rules, ontological reasoning, and SHACL validation guarantee 100% auditability on compliance, eligibility, and dependency chains where probabilistic LLMs guess.

CRO & Unit Economics • Part 6 of 6 • Coming Soon

Knowledge Graphs in Production: Querying, Token Economics, and Agent Workflows

Connecting AI agents to live SPARQL endpoints—featuring an interactive public graph showcase—to cut prompt token volume by 90%+ while eliminating hallucinations.