Unified biodiversity discovery across all evidence types
Biologists can query survey records, camera trap evidence, acoustic detections, and report citations together instead of searching separate systems and attachments.
Making 30 Years of Biodiversity Data AI-Ready — in a Single Platform
A government department unified decades of structured and unstructured biodiversity data into one governed knowledge graph, making 6M+ species records searchable, secure, and ready for GraphRAG.
Government & environmental scienceIndustry: Government / Environmental ScienceProducts used: Fluree ITM, CAM, Sense, and Core with GraphRAG
Customer Snapshot
- Industry
- Government / Environmental Science
- Products used
- Fluree ITM, CAM, Sense, and Core with GraphRAG
- Dataset
- 6M+ species records with 250,000+ new observations annually
- Asset value
- AUD $200M+ replacement value
- Environment
- Oracle, Microsoft Access, Excel, plus decades of unstructured field content
- Goal
- Create a single governed platform for structured data, unstructured content, and AI-ready biodiversity intelligence
The Challenge
Decades of biodiversity knowledge were locked in legacy systems and disconnected field content.
The department depended on a critical biodiversity knowledge base across more than 60 applications, but legacy databases and spreadsheets could not support modern ingestion, security, provenance, or AI. Even more valuable evidence lived in camera trap images, audio, video, and scanned reports that were invisible to analytics and AI.
- Rigid legacy schemas could not model evolving taxonomies or rich semantic relationships.
- Unstructured field evidence was disconnected from the structured platform and impossible to query holistically.
- Sensitive species data required granular governance, auditability, and public-safe access controls.
The Approach
1
Golden ontology management with Fluree ITM
The department established a governed semantic foundation for taxonomic models, harmonized terms, version control, and full audit history across ontology changes.
2
Structured data mapping with Fluree Sense
Legacy Oracle, Access, and spreadsheet data were classified, validated, and mapped into the semantic model with remediation workflows for exceptions.
3
Unstructured content extraction with Fluree CAM
Camera trap images, bioacoustic recordings, video, scanned PDFs, and reports were processed with OCR and LLM pipelines to extract species, locations, dates, and relationships.
4
Governed knowledge graph delivery in Fluree Core
The resulting graph delivered immutable history, SPARQL and API access, role-aware security, and GraphRAG-powered natural-language answers for non-technical conservation staff.
Use Cases In Action
GraphRAG for conservation and research questions
Researchers and officers can ask complex natural-language questions about species, sites, seasons, methods, and evidence trails and receive contextual, governed results.
Sensitive-species governance by design
The platform automatically obscures precise coordinates for protected species in public contexts while preserving full fidelity for authorized administrators.
Business Outcomes
Structured + unstructured unification
6M+ structured records and decades of field content now live in one searchable semantic platform.
AI-ready access
Natural-language retrieval through GraphRAG gives non-technical users direct access to governed biodiversity intelligence.
Regulatory and scientific traceability
Immutable audit trails and time-travel query support improve compliance, provenance, and reproducibility.
Operational reach
More than 60 downstream applications are now served from a single knowledge foundation.
Why Fluree
Unifies structured records and rich unstructured content in one semantic knowledge graph.
Embeds governance and differentiated access control directly at the data layer.
Uses GraphRAG to answer from governed relationships instead of text fragments.
Preserves full provenance and immutable history for scientific and regulatory use cases.
Applicable To Your Organization If You…
- Manage long-lived public-sector or research datasets with evolving ontologies.
- Need to make image, audio, video, and document archives usable by analytics and AI.
- Require strict auditability and policy enforcement for sensitive or regulated data.
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