Research, Reports, and Guides
for Enterprise AI Leaders
Benchmark studies, technical whitepapers, implementation guides, and case studies — all grounded in real enterprise deployments.
Case Studies
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Building Marketing Intelligence Sovereignty
A governed AI Agent Factory for marketing intelligence, product knowledge, and social response workflows.
Streamlining Distribution and Access to Trends through a Data Portal
An AI-driven data portal architecture that automates semantic tagging, topic discovery, and search relevance.
Making 30 Years of Biodiversity Data AI-Ready — in a Single Platform
A biodiversity knowledge platform that unifies structured records and field content into one governed GraphRAG-ready graph.
Latest from the Blog
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Fluree Launches Verifiable Knowledge Graph Database for Agentic AI
Fluree today announced the general availability of FlureeDB — a semantic graph database engineered for an era in which every answer, human or machine, must be fast, defensible, and traceable to its source.
Inside FlureeDB: The Features That Make a Knowledge Graph Verifiable
Most databases store records. FlureeDB stores knowledge — and the proof of where it came from. A walk through the features that make a knowledge graph verifiable: immutability, provenance, per-triple security, time travel, and more.
Context-in-the-Loop: The Architecture for Autonomous AI
AI agents fail not because they lack intelligence but because they lack context. Context-in-the-Loop is the next architectural step after HITL and RAG.
Whitepapers, Guides & Reports
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AI Playbook for Regional Banks
How regional and community banks can deploy AI grounded in their own customer, transaction, and servicing data — with governance and provenance built in.
Semantic GraphRAG: A Complete Overview
The full story on Semantic GraphRAG — why knowledge graphs outperform vector-only RAG, the architecture behind Fluree’s approach, and how to put it into production.
Knowledge Graphs for Analytics
How knowledge graphs extend traditional analytics pipelines — enabling cross-source queries, richer context, and AI-ready semantic data.
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