Trusted cross-channel pricing analytics
Resolved product identities make price comparisons reliable across retailers and channels instead of distorted by duplicates.
How a Global 500 CPG Company Built an AI-Ready Product Catalog with 97.5% Accuracy — in 3 Weeks
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.
Consumer packaged goodsIndustry: Consumer Packaged GoodsScale: Global 500 company with 20+ billion-dollar brands
Customer Snapshot
- Industry
- Consumer Packaged Goods
- Scale
- Global 500 company with 20+ billion-dollar brands
- Products used
- Fluree Sense
- Durable asset
- A governed product ontology (categories, attributes, equivalence rules) reused by every downstream team and agent
- Data scope
- 5 disparate retailer and third-party product sources
- Primary challenge
- Create a single trusted product catalog from inconsistent, incomplete, high-volume product data
The Challenge
Massive product data volume was slowing the business because it was incomplete, inconsistent, and full of duplicates.
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.
- 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.
The Approach
1
Golden record creation across five sources
Fluree classified, deduplicated, and linked records across all source systems, assigning unifying master IDs with lineage back to every contributing source.
2
AI-powered attribute inference
Fluree Sense inferred missing product details like GTIN14, sub-categories, packaging types, sizes, and flavors that manual processes had missed.
3
Data quality benchmarking and exception workflows
Quality rules were applied at both ends of the flow so subject matter experts could review flagged exceptions while machine learning continuously improved.
4
An ontology, not a rulebook
Rather than hard-coding matching rules, Fluree formalized the customer’s 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.
Use Cases In Action
AI-ready consumer insights
Enriched and classified product data supports better segmentation, trend analysis, and demand modeling.
Supply chain and fulfillment planning
Consistent master product data improves forecasting, inventory management, and operational planning across brands.
Business Outcomes
Accuracy at scale
The unified product catalog achieved 97.5% accuracy across five inconsistent source systems.
Hidden duplicate problem exposed
Fluree surfaced and resolved a 48% duplicate rate that had not been quantified before.
Speed to production
An end-to-end intelligent catalog was delivered in three weeks instead of months.
Enrichment with provenance
Every golden record preserved traceability to source systems while filling critical attribute gaps.
An ontology that outlives the project
The category framework, attribute definitions, and equivalence rules now live as a governed, versioned vocabulary. New sources, new analytics, and new AI agents classify against the same model instead of starting over.
Why Fluree
Combines classification, deduplication, enrichment, and taxonomy alignment in one workflow.
Maintains lineage from each golden record back to every underlying source system.
Uses subject matter expert feedback to keep machine learning accurate in nuanced product categories.
Treats the ontology as a first-class, governed asset (the same discipline Fluree ITM brings to enterprise taxonomy management), so the semantic model compounds instead of dissolving when the project ends.
Delivers AI-ready product foundations fast enough for operational teams to act on.
Applicable To Your Organization If You…
- Need product master data or golden records across fragmented commerce channels.
- Have high-value analytics blocked by duplicate records and missing attributes.
- Want to compress months of remediation into weeks without sacrificing trust.
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