Connect Scientific Meaning Across Data, Systems, and Decisions
Xendat helps life-sciences organizations build governed, FAIR-aligned data and semantic capabilities that support research, interoperability, analytics, knowledge reuse, and trustworthy AI.
The challenge
Scientific data is created across specialized instruments, applications, studies, domains, partners, and public vocabularies. Differences in terminology, models, reference values, ownership, and lifecycle practices make information difficult to connect and reuse.
How Xendat helps
- FAIR data maturity, strategy, and roadmap
- R&D data governance and stewardship
- Ontology strategy, development, and governance
- Scientific terminology, controlled vocabularies, and reference data
- Semantic mapping and harmonization
- Metadata, lineage, catalog, and data discovery
- Data-quality requirements and controls
- Architecture and interoperability across research systems
- Knowledge graphs and semantic context for analytics and AI
- Specialized ontology, analyst, and engineering teams
Representative use cases
- R&D ontology capability. Establish the roles, governance, processes, architecture, and platform required to create, maintain, publish, and reuse scientific ontologies.
- FAIR data transformation. Assess FAIR maturity, identify barriers to findability, accessibility, interoperability, and reuse, and create a phased improvement roadmap.
- Application semantic models. Design shared semantic models that connect scientific concepts across applications, integration pipelines, data platforms, and analytical products.
- Controlled terminology and reference lists. Replace scattered spreadsheets with governed ownership, definitions, mappings, approvals, versioning, and publication.
- Semantic migration and harmonization. Align terminology and models during application migration, platform consolidation, organizational change, or acquisition.
- AI-ready scientific context. Provide approved terminology, relationships, and context that can support more consistent search, analytics, agents, and copilots.
Deep life-sciences expertise
Xendat brings deep practitioner experience in pharmaceutical R&D ontology, FAIR data, semantic integration, governance, architecture, and implementation.
Governed semantics for life-sciences data.
AxiomatIQ is designed to help organizations model, govern, harmonize, publish, and operationalize enterprise semantics. Its modular capabilities can support ontology development, semantic refinement, business reference lists, broad semantic discovery, and controlled semantic context for AI.
Start a life-sciences strategy conversation.
Tell us about the research data, FAIR, ontology, or semantic challenge you are working to address.