Fluree

Looking back at one of our favorite interviews with The Knowledge Graph Conference (via Ellie Young and Brian Platz). There's lots of wisdom on #datacentricity packed into this hour! https://lnkd.in/ehd9Fx6F #knowledgegraph #data #fluree

All Of Your Data and Content, Ready for AI: Fluree helps organizations align their data from any source to a universal data model for better apps, agents, and analytics. Learn more: https://lnkd.in/eaNW9MUT 

FAIR data principles (Findable, Accessible, Interoperable, Reusable) become operational reality through knowledge graphs. Organizations can consolidate data silos, migrate to new platforms, or build master data management systems while maintaining semantic consistency across diverse sources. The result: advanced analytics, recommendation systems, and semantic search engines that understand business context. https://lnkd.in/dhx7i-ir #FAIRData #DataInteroperability #MDM #SemanticSearch #EnterpriseData

Last week at the AI in Capital Markets Summit, I sat down with Eliud Polanco from Fluree to dig into "Solving Complex AI Use Cases in the Real World"—because nothing brings you back to earth faster than trying to deploy AI in actual enterprise environments. Here's what we unpacked: 1. The messy reality of GenAI business requirements: Remember when we used to get 10-page business requirements documents with detailed data specs and wireframes? Those days are over. Now it's more like: "Can you make our post-trade settlement process faster with AI?" Eliud walked me through how their platform actually tackles these vague requests by systematically breaking them down while leveraging all that institutional knowledge you've accumulated over the years. 2. The data silo problem nobody talks about: Your AI agent needs to make sense of your structured trade data AND that critical PDF buried in someone's shared folder. Fluree's approach integrates both worlds instead of pretending unstructured data doesn't exist. 3. A real six-week implementation story: We went through an actual capital markets case where a client went from "we want an AI agent for post-trade settlement" to having something deployed and working in production in six weeks. Not six months. Here is what actually made it happen: - Graph database that can handle the messiness of real enterprise data - Auto-tagging that turns document chaos into something searchable - Security built in from day one (because this is capital markets, not a sandbox) What impressed me most was how grounded the conversation was. No AI magic here — just practical solutions for the unglamorous but critical work of getting enterprise data ready for intelligent agents. Big thanks to Eliud and the Fluree team for keeping it transparent, technical, and refreshingly grounded. #AICMSummit

When disparate data turns into semantic knowledge, we can implement GraphRAG for: 1. Trusted Interactions 2. Smart Data Agentic Systems 3. Analytics never before possible Try Fluree out: www.flur.ee/get-started

SPARQL queries enable LLMs to pull specific data points from knowledge graphs that are directly relevant to user questions, rather than processing entire document sets. This precision dramatically improves both retrieval and generation capabilities. For regulatory compliance in pharmaceuticals, this means connecting clinical trials, guidelines, and safety records to generate comprehensive, accurate reports that reduce critical decision-making risks. https://lnkd.in/ef6h5qdU #SPARQL #PrecisionAI #RegulatoryAI #KnowledgeGraphs #DataPrecision

McKinsey reports: "Managing data remains a top barrier to value creation from GenAI." The issue isn't model capability—GPT-4o mini and Claude 3.5 Sonnet offer greater intelligence at lower cost. The bottleneck is enterprise data architecture that can't provide contextual, trusted, real-time information at scale. Universal ontologies organized through knowledge graphs address this fundamental scaling barrier. https://lnkd.in/emjA_6ir #GenAIScaling #DataArchitecture #EnterpriseAI #KnowledgeGraphs #AIInfrastructure

In financial services, when querying market data, LLMs can use linked data standards to connect transactional information with market analysis, providing predictions based on comprehensive, well-connected datasets. This contextual grounding dramatically improves decision-making quality. A Fortune 500 financial firm used this approach to create analyst co-pilots that surface context-aware insights from over 500,000 internal documents. https://lnkd.in/ef6h5qdU #FinTech #WealthManagement #AIAnalytics #ContextualAI #FinancialServices

Regulatory compliance in pharmaceuticals involves connecting clinical trials, regulatory guidelines, and drug safety records across multiple domains. To classify and unify data from global regulatory systems, a large multinational pharmaceutical organization used Fluree's technology, expediting automated ECTD filing processes. The result: streamlined risk identification, enhanced safety monitoring, and more efficient regulatory reporting that accelerates therapeutic time-to-market. https://lnkd.in/eAHsS3cX #RegTech #PharmaAI #ComplianceAutomation #KnowledgeGraphs #LifeSciences

Join us tomorrow! Webinar: The Future of RAG — Graph-Native AI with Fluree and MCP In this session, we’ll explore how combining GraphRAG’s contextual retrieval with MCP’s agentic interface allows teams to skip traditional, slow-moving analytics efforts and instead deliver high-precision, scalable intelligence across the enterprise. What You'll Learn: 🔹What is GraphRAG? A method of retrieval-augmented generation built for structured and semantic data. 🔹What is MCP? A protocol for grounding AI agent interactions in contextual, structured knowledge. 🔹How they work together to reduce complexity, increase accuracy, and enable secure, explainable AI workflows. 📅 Details & signup: https://lnkd.in/epE38EA8 #KnowledgeGraph #RAGStack #SemanticAI #GraphRAG #Fluree #MCP