Blog

Insights & Updates

Thought leadership on enterprise AI, knowledge graphs, product updates, and engineering best practices.

Thought LeadershipMarch 17, 2026·20 min read

How to Build a Semantic Layer for Enterprise AI

Only 7% of enterprises have AI-ready data. A knowledge graph semantic layer breaks the 80% accuracy ceiling — and here is the architecture to build one.

Kevin DoubledayRead article

Thought LeadershipFeb 26

Introducing Business Intelligence Without the BI Software

For all of its advanced capabilities and $30 billion market size, business intelligence (BI) still doesn’t get used much by companies with less than 5,000…

Kevin Doubleday7 min

Thought LeadershipFeb 10

GraphRAG vs. Vector RAG: When Knowledge Graphs Outperform Semantic Search

Vector RAG fails when answers live in connections, not content. Seven scenarios where GraphRAG traversal decisively outperforms semantic similarity search.

Kevin Doubleday10 min

Thought LeadershipJan 29

Why GenAI Security Starts at the Data Layer

AI agents on uncontrolled data inherit every SaaS vulnerability. Data-centric security that embeds policies at the data layer is the only durable fix.

Kevin Doubleday5 min

Thought LeadershipDec 8

How Semantic Data Accelerates CMC Regulatory Compliance in LifeSciences

Semantic ontologies map CMC data across LIMS, ERP, and MES — compressing regulatory timelines and building reusable drug manufacturing knowledge assets.

Kevin Doubleday4 min

Thought LeadershipNov 17

Enterprise AI Accuracy: Guide to Reliable Systems

GraphRAG and semantic knowledge graphs break the 80% accuracy ceiling that stalls enterprise AI — a guide to reliable, hallucination-resistant AI systems.

Kevin Doubleday25 min

Enterprise AINov 4

Why Enterprise AI Hallucinates (And How to Fix It)

Traditional RAG plateaus at 80% accuracy. Semantic GraphRAG reaches 95%+ by feeding LLMs explicit relationships instead of fragmented chunks.

Kevin Doubleday23 min

Enterprise AIOct 16

GraphRAG & Knowledge Graphs: Making Your Data AI-Ready for 2026

78% of businesses lack AI-ready data. Knowledge graphs and GraphRAG close the gap between AI ambitions and the siloed reality of enterprise data.

Kevin Doubleday6 min

ProductJul 21

Understanding Intelligent Document Extraction with Fluree: Part 1 – LLMs

Fluree pairs knowledge-graph governance with top LLMs to turn unruly PDFs into traceable, reusable knowledge assets — cryptographic provenance included.

Arnaud Cassaigne6 min

ProductJul 21

Understanding Intelligent Document Extraction with Fluree: Part 2 – Hybrid Models

Not every document extraction task needs a frontier LLM. Fluree benchmarks Gemini, Claude, Llama, and Mistral OCR to show which model earns its cost.

Arnaud Cassaigne6 min

Enterprise AIJul 16

Reshaping Business Intelligence with GraphRAG, MCP, and LLMs

For all the hype around AI, MCP (Model Context Protocol) actually seems to be delivering on the promise of extracting insights from data.

Brian Platz6 min

Thought LeadershipJun 10

Controlling LLMs with Enterprise Taxonomies

LLM reliability hinges on the data it is grounded in. Enterprise taxonomies with SKOS, version control, and real-time APIs are the unglamorous key.

Kevin Doubleday4 min

Enterprise AIJun 10

For AI Business Transformation, Look Beyond SaaS

Siloing AI inside SaaS apps produces efficiency, not transformation. Business AI value requires unified data and context — not more point-solution agents.

Kevin Doubleday4 min

Thought LeadershipApr 25

Automating Regulatory Compliance in Pharma with Knowledge Graphs

Drug authorization takes 40 people 24 months to compile. Knowledge graphs and semantic ontologies turn that burden into a reusable, regulatory-ready asset.

Kevin Doubleday4 min

Thought LeadershipMar 20

How to Create a Knowledge Graph for Analytics

Siloed data hides relationships behind real decisions. A guide to building a knowledge graph that turns disconnected enterprise data into analytic power.

Kevin Doubleday5 min

Thought LeadershipMar 3

Towards an Error-Free Enterprise LLM

Fine-tuning and prompt engineering hit a wall at enterprise scale. A universal ontology within a knowledge graph is what gets LLMs to near-100% accuracy.

Kevin Doubleday11 min

Thought LeadershipDec 19

Are Large Language Models Too Dominant?

LLMs dominate AI, but hallucinations, black-box reasoning, and massive compute costs expose real limits. Knowledge graphs and hybrid models fill the gaps.

Arnaud Cassaigne8 min

Enterprise AIOct 16

How Linked Data Can Help Improve LLM Accuracy and Effectiveness

Linked data standards — RDF, SPARQL, OWL — give LLMs a map instead of a guess, improving RAG accuracy and unifying siloed enterprise knowledge for GenAI.

Kevin Doubleday6 min

Company NewsSep 16

Fluree Expands European Presence Through Strategic Partnership with Semantic Partners

This partnership represents Fluree’s commitment to broadening its reach in Europe. Semantic Partners, with its headquarters in London and a team across…

Kevin Doubleday2 min

Free weekly newsletter

Stay ahead of the curve

Weekly insights on enterprise AI, knowledge graphs, and data intelligence — straight to your inbox.