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.
Industry · Consumer packaged goods
One intelligence layer for CPG, from supplier contract to shelf.
A CPG intelligence layer is one governed knowledge graph that connects trade promotion, revenue growth management, product, and supply data, so people and AI agents decide from the same facts. Fluree builds it on FlureeDB, its verifiable knowledge graph database, with every fact traceable to the contract, system, or document it came from.
Works with
Why do CPG decisions run on a partial picture?
Because the context for one decision is spread across five systems and five teams. Every week brings retailer negotiations, trade allocations, reprices, and reroutes, and the context for each one sits in a different system.
Trade spend is optimized blind.
PwC’s Strategy& puts trade spend at about 20% of gross sales, typically the second-largest line on the P&L after cost of goods sold (Zero-based trade for CPG leaders, 2017). Yet the context a negotiation needs lives outside the trade system: contracts in legal repositories, product economics in ERP, commodity exposure in procurement.
Product truth is fragmented.
Product data arrives from retailers, distributors, and data providers in inconsistent, duplicated form. One Global 500 CPG company found that 48% of its product records with unique IDs were duplicates, and every 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.
Revenue Growth Management
What does CPG revenue growth management need from your data?
Revenue growth management (RGM) grows profitable revenue through pricing, promotions, pack-price architecture, mix, and trade terms. It needs the whole margin equation in one place: trade spend and account profitability on the customer side, product costs, contract terms, and commodity exposure on the supply side.
Most RGM teams see half of it. The supply side lives in procurement, legal, and ERP, so when cocoa jumps or a supplier slips, the promotion calendar is the last to know. On one intelligence layer, promotion performance, trade spend, account profitability, and commodity exposure enter every negotiation together, and AI agents stay inside JBP and contract terms because policy is enforced in the data.
Test it on a working model: the Consumer Goods demo kit asks which live promotions still clear their margin guardrail after cocoa rose about 50%, and answers from trade, cost, and commodity data in one graph.
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.
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 master data 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 did a Global 500 CPG company build an AI-ready product catalog in three weeks?
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.
Consumer Goods ships as a working knowledge graph: 35 classes, 413 entities, 6 JSON-LD files, and 26 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 6 JSON-LD 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 26 competency questions to the AI agent in plain English and check its answers against the expected results.
CPG intelligence layer questions, answered.
Short answers to what CPG commercial, supply, and data leaders ask us most.
Does Fluree replace our TPM, RGM, or ERP systems?
No. Fluree sits underneath them. It connects the context each system lacks, such as contract terms, product costs, and commodity exposure, in one governed graph that your existing tools and AI agents can query.
How can AI agents support revenue growth management?
Agents can assemble the context for a negotiation, test promotion scenarios against margin guardrails, and flag commodity or supplier risk before it reaches the promotion calendar. They need governed data to do it safely: on Fluree, an agent’s queries run under the same access policy as a person’s, and its answers trace back to the facts behind them.
What makes a CPG product catalog AI-ready?
One golden record per product, complete attributes such as GTIN-14, pack size, and flavor, and a governed product ontology that every team and agent classifies against. In a published Fluree case study, a Global 500 CPG company reached 97.5% catalog accuracy across five sources in three weeks.
Can AI agents see confidential retailer terms?
Only if policy allows it. FlureeDB enforces access rules on every query, down to individual facts, so an agent working one retailer’s plan can be barred from another retailer’s terms. The same rules apply to people and agents.
How long does it take to get started?
Most teams ship a working, governed foundation in four to eight weeks by starting with one decision domain, such as a category’s trade promotions or a region’s product catalog. To explore first, the Consumer Goods demo kit loads into a free Fluree ledger in about two minutes.
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.
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