Composable enterprise tagging system
Fluree combined knowledge graphs with AI to apply corporate knowledge consistently to unstructured content, improving relevance and completeness for search and discovery.
Streamlining Distribution and Access to Trends through a Data Portal
A financial services leader used Fluree to automate tagging, improve discovery, and grow a data portal into a more trusted intelligence destination.
Financial servicesCustomer: Global financial services leaderIndustry: Investment banking, wealth management, and institutional securities
Customer Snapshot
- Customer
- Global financial services leader
- Industry
- Investment banking, wealth management, and institutional securities
- Core use case
- Digital publishing, content tagging, and search discovery
- Content scale
- Nearly half a million documents and emails indexed
- Primary challenge
- Automate semantic tagging and discover emerging topics across unstructured assets
- Solution
- Reference knowledge graph + AI tagging + continuous topic discovery
The Challenge
Publishing workflows were not turning document volume into discoverable intelligence.
The client’s analyst team produced a vast volume of documents and emails with critical investment intelligence, but its workflow did not fully use the knowledge graph to improve tagging, search, and discovery for portal users.
- Manual and inconsistent semantic tagging reduced relevance for business queries.
- Emerging topics were not being surfaced quickly enough to grow the portal.
- Business users needed a modern search experience that could keep pace with market change.
The Approach
1
Unify data modeling
Create a source of truth with enterprise ontology, topical taxonomies, and named entity datasets.
2
Integrate AI tagging into publishing
Automatically process published documents for tagging, inference, disambiguation, and relevance scoring.
3
Automate continuous discovery
Detect and score new topics in real time, then feed validated findings back into the knowledge model.
"
By integrating Fluree into its content supply chain, the client eliminated error-prone tagging, expanded its knowledge base tenfold, and regained the trust of portal users consuming the data.
Source: financial services data portal case study
Use Cases In Action
Continuous topic mining for portal growth
The system uncovers hidden patterns and relationships in documents, enriching the portal without manual curation.
Business Outcomes
Tagging quality
Replaced error-prone manual workflows with knowledge-controlled, AI-assisted tagging.
Discovery
Improved exploration and search with richer semantic relevance after publication.
Knowledge growth
Expanded the knowledge base tenfold through automated detection and validation of new topics.
User trust
Restored confidence by improving the quality and consistency of tagged intelligence.
Why Fluree
Reference knowledge graph aligned ontology, taxonomies, and named entities in one source of truth.
AI-driven tagging automated inference, disambiguation, and relevance scoring in the publishing chain.
Continuous topic extraction surfaced new patterns from unstructured content and fed them back into the graph.
The architecture improved search relevance without relying on manual curation.
Applicable To Your Organization If You…
- Publish or manage large volumes of unstructured content that need better tagging and retrieval.
- Need semantic search experiences grounded in enterprise taxonomies and controlled knowledge.
- Want automated discovery of emerging topics instead of manual content curation.
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