Industry
One Intelligence Layer, From Supplier Contract to Shelf
The full CPG guide: why copilots per silo harden fragmentation, and how one governed graph connects trade promotion, product, supply, and the plant floor.
One intelligence layer, from supplier contract to shelf.
CPG may be the most decision-dense industry there is. Every week brings retailer negotiations, trade allocations, reprices, and reroutes, and the context for each one sits in a different system. Fluree connects trade, ERP, PLM, and CRM context into one governed knowledge graph, so revenue growth management, product data, sourcing, and the plant floor work from the same picture.
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The value chain can’t see itself.
Trade spend, contracts, product economics, commodity movements, and supplier terms each live with their own team, in their own system. One decision needs all of them. Three problems every CPG data leader knows by heart.
Trade spend is optimized blind.
Trade runs 15–25% of gross sales, the second-largest line on the P&L. But the context a negotiation needs lives outside the trade system: contracts in legal repositories, product economics in ERP, commodity exposure in procurement. Revenue growth management works from a partial picture.
Product truth is fragmented.
Product data arrives from retailers, distributors, and data providers in inconsistent, duplicated form. One Global 500 CPG found that 48% of product records with unique IDs were duplicates. Every downstream pricing, promotion, and inventory decision inherited that distortion.
Agents raise the stakes.
A copilot bolted onto each silo gives you five confident, conflicting answers about the same SKU. AI agents can’t walk down the hall to ask whether a number looks right. Whatever fragmentation lives in your data, they operate on it at machine speed.
From five fragments to one governed commercial graph.
The same governed graph sits underneath four CPG outcomes. Start with the decision domain that hurts most; every later use case inherits the work.
Agentic revenue growth management
Promotion performance, trade spend, account profitability, and commodity and supplier exposure enter every negotiation together. Agents honor JBP and contract terms because policy is enforced at the data layer. The whole margin equation, customer side and supplier side, gets evaluated as one loop.
An AI-ready product catalog
Resolve duplicates across retailer, distributor, and internal feeds into governed golden records, with package sizes, flavors, and standards like GTIN-14 filled in and verified. The result is product truth that promotions, syndication, and agents can stand on.
Supply, sourcing & BOM intelligence
Standardize component and material descriptions across plants and supplier catalogs so equivalent parts are findable in minutes. Supplier concentration risk surfaces before it becomes a crisis, and early warnings buried in documents connect to the same graph.
An AI Agent Factory
A repeatable operating model for creating and running governed agents: conversational marketing mix, product knowledge, customer 360 in weeks instead of quarters. The semantic foundation is reusable, so the second agent costs a fraction of the first.
How a Global 500 CPG Company Built an AI-Ready Product Catalog with 97.5% Accuracy — in 3 Weeks
Working with Fluree, the company unified five disparate product-data sources, surfaced a 48% duplicate rate, and reached 97.5% product-data accuracy, with golden records in production in three weeks.
97.5%
Product-data accuracy across the unified catalog
48%
Duplicate rate discovered and resolved
3 weeks
From five raw sources to production golden records
Load the demo graph on a fresh ledger.
The story on this page ships as a working knowledge graph: a GS1-aligned model, a fictional commercial layer with a documented cocoa supply crisis, a real USDA branded-food corpus, an upstream traceability module on GS1 EPCIS 2.0, and eighteen packaged competency questions. Load it, ask, verify.
- 01
Create a free workspace
Start free on Fluree AI, no credit card, and create an empty database. Developers can use the Fluree CLI instead.
- 02
Import the graph
Load the four data files, model first. The whole dataset commits in seconds; the kit's README walks through it click by click.
- 03
Ask the questions
Put the ten competency questions to the AI agent in plain English and check its answers against the expected results.
Field notes on decision-grade CPG data.
Writing from the Fluree team on trade promotion, grounding, and agent memory. This page is built on that thinking.
Enterprise AI
Grounding LLMs on Solid Knowledge
Why retrieval over fragmented data plateaus below decision-grade accuracy, and what grounding in a governed graph changes.
Enterprise AI
Building Intelligent Memory for AI Agents
Agents need persistent, governed context that outlives a session: the enterprise memory every new agent inherits instead of rebuilding.
Start with one decision.
Pick one high-value domain: a category’s trade promotions, a region’s product catalog, a plant’s maintenance triage. Most teams ship a working, governed foundation in four to eight weeks.