index-BWlUU3al.js

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88ict auditability and policy enforcement for sensitive or regulated data."],cta:{title:"Need to modernize a complex public-sector knowledge platform?",description:"See how Fluree helps agencies unify structured and unstructured evidence into governed AI-ready knowledge graphs.",buttonLabel:"Explore government solutions",buttonHref:"/solutions/government"}},cpgCatalog:{slug:"cpg-ai-ready-product-catalog",image:"/industry-badges/manufacturing.webp",industry:"Consumer packaged goods",eyebrow:"Customer case study",title:"How a Global 500 CPG Company Built an AI-Ready Product Catalog with 97.5% Accuracy â in 3 Weeks",summary:"A Global 500 CPG company used Fluree Sense to unify five disparate product data sources, discover a 48% duplicate rate, and build enriched golden product records in just three weeks. The work also left behind a governed product ontology the company still runs on.",stats:[{value:"97.5%",label:"catalog accuracy"},{value:"48%",label:"duplicates discovered"},{value:"3 weeks",label:"time to production"},{value:"5",label:"sources unified"}],impactAtGlance:[{value:"97.5%",label:"data accuracy"},{value:"3 weeks",label:"end-to-end deployment"},{value:"Months â Days",label:"manual remediation reduced"}],snapshot:[{label:"Industry",value:"Consumer Packaged Goods"},{label:"Scale",value:"Global 500 company with 20+ billion-dollar brands"},{label:"Products used",value:"Fluree Sense"},{label:"Durable asset",value:"A governed product ontology (categories, attributes, equivalence rules) reused by every downstream team and agent"},{label:"Data scope",value:"5 disparate retailer and third-party product sources"},{label:"Primary challenge",value:"Create a single trusted product catalog from inconsistent, incomplete, high-volume product data"},{label:"AI payoff",value:"Pricing, consumer insights, and supply chain analytics built on clean golden records"}],challenge:{title:"Massive product data volume was slowing the business because it was incomplete, inconsistent, and full of duplicates.",description:"The company needed one intelligent product catalog across online retailers, brick-and-mortar channels, and external providers, but analysts were spending months manually reconciling inconsistent records and missing attributes before the data was useful.",points:["Critical product nuances like packaging, size, and flavor were missing or inconsistently captured.","Nearly half the records that appeared unique were actually duplicates spread across systems.","Downstream pricing, demand, and supply chain analytics were bottlenecked by data prep delays."]},approach:[{title:"Golden record creation across five sources",description:"Fluree classified, deduplicated, and linked records across all source systems, assigning unifying master IDs with lineage back to every contributing source."},{title:"AI-powered attribute inference",description:"Fluree Sense inferred missing product details like GTIN14, sub-categories, packaging types, sizes, and flavors that manual processes had missed."},{title:"Data quality benchmarking and exception workflows",description:"Quality rules were applied at both ends of the flow so subject matter experts could review flagged exceptions while machine learning continuously improved."},{title:"An ontology, not a rulebook",description:"Rather than hard-coding matching rules, Fluree formalized the customerâs master category framework into a working product ontology: classes, attributes, and equivalence rules that every classification decision referenced. Products were tagged against that shared model, so segmentation and analytics stayed consistent downstream."}],useCases:[{title:"Trusted cross-channel pricing analytics",description:"Resolved product identities make price comparisons reliable across retailers and channels instead of distorted by duplicates."},{title:"AI-ready consumer insights",description:"Enriched and classified product data supports better segmentation, trend analysis, and demand modeling."},{title:"Supply chain and fulfillment planning",description:"Consistent master product data improves forecasting, inventory management, and operational planning across brands."}],outcomes:[{title:"Accuracy at scale",description:"The unified product catalog achieved 97.5% accuracy across five inconsistent source systems."},{title:"Hidden duplicate problem exposed",description:"Fluree surfaced and resolved a 48% duplicate rate that had not been quantified before."}

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88],outcomes:[{title:"Time-to-value",description:"New analytics use cases went live in 3 weeks instead of 9 months."},{title:"Cost reduction",description:"The transformation reduced manual labor costs by $3M through automation."},{title:"Platform simplification",description:"10+ competing warehouses were consolidated into one unified cloud platform."},{title:"Strategic AI readiness",description:"The company built a trusted data layer capable of supporting future AI initiatives across the business."}],whyFluree:["Automates the hardest part of transformation: getting raw source data into trustworthy analytic shape.","Works with existing analytics environments instead of forcing a rip-and-replace motion.","Combines data inventory, organization, cleansing, and classification in one platform.","Moves teams from manual data wrangling to repeatable AI-ready operations."],applicability:["Are modernizing pharma, clinical, or regulated enterprise data across many warehouses.","Need faster analytics delivery but keep getting blocked by upstream data quality issues.","Want an AI-ready foundation before scaling advanced models or assistants."],cta:{title:"Need to accelerate AI-ready data transformation in pharma?",description:"Explore how Fluree Sense helps regulated organizations clean, classify, and deliver enterprise data faster.",buttonLabel:"Explore pharma solutions",buttonHref:"/solutions/pharma"}},communityBank:{slug:"community-bank-ai-ready-golden-records",image:"/industry-badges/banking.webp",industry:"Financial services / banking",eyebrow:"Customer case study",title:"How a Community Bank Built AI-Ready Golden Records to Power Proactive Customer Outreach",summary:"A community bank used Fluree Sense to unify 3â5 competing reporting systems, create golden records for 330,000+ customers, and enable proactive banker conversations powered by trustworthy household-level data.",stats:[{value:"330,000+",label:"customers unified"},{value:"113",label:"branches served"},{value:"3â5",label:"reporting systems unified"},{value:"1,400+",label:"employees supported"}],impactAtGlance:[{value:"330,000+",label:"golden customer records"},{value:"Single",label:"source of truth established"},{value:"AI-driven",label:"entity resolution"}],snapshot:[{label:"Industry",value:"Financial Services / Banking"},{label:"Scale",value:"113 branches, 330,000+ customers, 1,400+ employees"},{label:"Products used",value:"Fluree Sense"},{label:"Primary challenge",value:"Customer, household, and reporting data disagreed across systems"},{label:"Goal",value:"Build a single source of truth that supports proactive outreach and trusted reporting"},{label:"Outcome",value:"Golden records, cleaner point-of-entry data, and stronger banker conversations"}],challenge:{title:"The bank wanted proactive customer intelligence, but inconsistent records made even simple questions unreliable.",description:"Different departments had implemented their own reporting tools, creating multiple competing versions of the truth. At the same time, the bank could not reliably determine household relationships across customer identifiers, limiting any effort to use data proactively.",points:["3â5 reporting systems produced conflicting answers from the same underlying business activity.","Household relationships were hard to resolve because customers existed under multiple identifiers across systems.","Without trusted records, AI or analytics would only automate inconsistency."]},approach:[{title:"Entity resolution and household golden records",description:"Fluree Sense resolved which identifiers represented the same person, household, or relationship, creating golden records with complete lineage."},{title:"Automated quality controls at data entry",description:"Point-of-entry controls and external reference validation, including postal checks, improved source quality before issues spread downstream."},{title:"AI-driven mastery instead of brittle decision trees",description:"The bank used Flureeâs learning-based approach to avoid the constant breakage and maintenance associated with rule-heavy decision trees."},{title:"Single source of truth for enterprise consumption",description:"The resulting repository delivered consistent data whether consumed

88in spreadsheets, existing business apps, or the enterprise warehouse."}],useCases:[{title:"Proactive retail and commercial outreach",description:"Bankers can identify customer behaviors and surface better product conversations using trusted household and transaction context."},{title:"Consistent financial reporting",description:"Balance sheets, income statements, and downstream reporting draw from the same trusted records instead of conflicting systems."},{title:"Customer-centric data culture",description:"The transformation improves not only systems, but also how teams think about getting data right from the first touchpoint."}],outcomes:[{title:"Golden record foundation",description:"The bank established a single trusted source of truth across all transaction systems and lines of business."},{title:"Household intelligence",description:"330,000+ customer records can now be understood and used in context at the household level."},{title:"Operational trust",description:"Reporting and data consumption are more consistent because teams are working from the same mastered data."},{title:"Human-centered AI readiness",description:"Clean data now supports proactive banker conversations instead of impersonal automated outreach."}],whyFluree:["Uses AI-driven entity resolution to solve customer and household identity at scale.","Improves data quality at the point of entry, not just after the fact.","Avoids brittle rule trees that disrupt master data operations when business logic changes.","Creates a durable source of truth that serves analytics, reporting, and future AI use cases."],applicability:["Operate in banking or financial services with fragmented customer and household records.","Need master data foundations before launching proactive AI or personalization efforts.","Want cleaner reporting and customer intelligence without adding more brittle logic layers."],quote:{text:"By providing AI-driven workflows that can scale to millions of records, Fluree Sense provides crucial input for Master Data Management, leading to not only a satisfied customer base, but to a customer-centric, empowered tomorrow.",attribution:"Chief Data Officer"},cta:{title:"Need trusted customer golden records before scaling AI in banking?",description:"See how Fluree helps banks unify fragmented systems into a governed customer intelligence foundation.",buttonLabel:"Explore financial solutions",buttonHref:"/solutions/financial"}}},vc=[{slug:R.marketingIntelligence.slug,industry:"Beauty & cosmetics",company:"Global beauty brand",title:R.marketingIntelligence.title,image:R.marketingIntelligence.image,description:"A governed AI Agent Factory for marketing intelligence, product knowledge, and social response workflows.",metrics:["5 core functions","MCP-ready","Zero-hallucination target"]},{slug:R.dataPortal.slug,industry:"Financial services",company:"Global financial services leader",title:R.dataPortal.title,image:R.dataPortal.image,description:"An AI-driven data portal architecture that automates semantic tagging, topic discovery, and search relevance.",metrics:["500K documents","10x knowledge base","Real-time discovery"]},{slug:R.governmentBiodiversity.slug,industry:"Government & environmental science",company:"Government department",title:R.governmentBiodiversity.title,image:R.governmentBiodiversity.image,description:"A biodiversity knowledge platform that unifies structured records and field content into one governed GraphRAG-ready graph.",metrics:["6M+ records","60+ apps","GraphRAG-ready"]},{slug:R.cpgCatalog.slug,industry:"Consumer packaged goods",company:"Global 500 CPG company",title:R.cpgCatalog.title,image:R.cpgCatalog.image,description:"An AI-ready product catalog with 97.5% accuracy, 48% duplicates discovered, and production delivery in 3 weeks.",metrics:["97.5% accuracy","48% duplicates","3 weeks"]},{slug:R.pharmaTransformation.slug,industry:"Pharmaceutical",company:"$2B+ pharmaceutical company",title:R.pharmaTransformation.title,image:R.pharmaTransformation.image,description:"A transformation program that consolidated 10+ warehouses and cut new anal

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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.