Predictive advertising spend optimization
A knowledge agent blends historical spend, predictive model outputs, and external factors so teams can ask natural-language questions about the investment needed to drive product and regional revenue goals.
Building Marketing Intelligence Sovereignty
A global beauty brand built a governed AI Agent Factory that turns fragmented marketing, product, and social knowledge into faster, more reliable decision support.
Beauty & cosmeticsCompany: Global beauty brandIndustry: Global beauty and cosmetics
Customer Snapshot
- Company
- Global beauty brand
- Industry
- Global beauty and cosmetics
- Headquarters
- Europe
- Primary challenge
- Adopt GenAI across marketing operations with accuracy, governance, and speed
- Solution
- Semantic knowledge graph + GraphRAG + MCP-based AI Agent Factory
- Business scope
- Marketing, communications, product intelligence, and social response workflows
The Challenge
Marketing intelligence was fragmented across systems, agencies, and channels.
The company needed AI agents that could answer high-value questions about ad spend, product positioning, and customer engagement, but the underlying knowledge lived across internal systems, data lakes, MarTech tools, social channels, and agency workflows.
- Optimize channel investment by brand and market.
- Integrate with external AI systems such as Google Vertex.
- Keep privacy, governance, and answer quality intact while scaling AI adoption.
The Approach
1
Knowledge model stewardship
Define semantic ontologies that give agents a structured understanding of the company’s business domain.
2
Data product creation
Build governed, quality-controlled data products from internal and external sources before exposing them to AI.
3
Knowledge integration
Map structured and unstructured data into the semantic model and connect it through the knowledge graph.
4
Agent administration
Build and tune knowledge agents against competency questions that prove answer quality.
5
DataOps engineering
Operationalize pipelines so agents answer from current, trustworthy knowledge.
"
Rather than building isolated point solutions, the customer and Fluree designed a comprehensive AI Agent Factory — a repeatable operating model for creating, governing, and deploying Knowledge Agents at scale.
Source: customer case study
Use Cases In Action
Product and social intelligence workflows
The same architecture supports richer product narratives for external AI systems and can be repurposed quickly for internal teams like social media response without rebuilding the foundation.
Business Outcomes
Time to market
Deploy new agents as fast as business demand requires instead of rebuilding the knowledge layer each time.
Accuracy
Answer from verified sources of truth with a zero-hallucination standard for enterprise workflows.
Timeliness
Continuously refreshed pipelines keep answers tied to the most current data available.
Data privacy
Role-based access stays with the data so agents never expose unauthorized knowledge.
Why Fluree
Semantic GraphRAG for explainable responses grounded in enterprise knowledge.
MCP connectivity across LLM platforms, including OpenAI and Google Vertex.
Built-in governance and role-based access controls that stay attached to the data.
Reusable ontology patterns to accelerate new markets and use cases.
Applicable To Your Organization If You…
- Need AI agents that must answer business questions across internal and external systems.
- Want a repeatable operating model instead of one-off GenAI experiments.
- Require strict privacy and accuracy controls before exposing enterprise knowledge to AI.
- Need to connect marketing, sales, product, and social signals through one semantic layer.
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