AI cannot run your business until the meaning of your business is modeled. Someone has to build that model. Today it gets built inside a vendor's platform, and it stays there. You rent it.
Axius SDC does it the other way. We turn the databases you already have into governed data assets, with meaning and provenance bound to the payload, in open standards, in your own store, on your own infrastructure. You own the meaning layer. And you can prove it.
Everyone now agrees the meaning layer is what makes AI work on your data. The only question left is who owns it.
Palantir Foundry, and the hyperscalers' semantic layers
Their engineers build the meaning layer for you, per customer, inside their proprietary software.
Your raw data is copied from your source systems, so the data itself is not what holds you. The ontology, the business logic, and the workflows are, and there is no export for them.
Deploying it on your premises keeps your data behind your walls. The meaning still lives in their software. That is location sovereignty, not ownership sovereignty.
SDCStudio components and SDC_Agents, into the store you already run
Your domain experts model the meaning once, as reusable components you keep. Not an ontology hand-built per customer, per database.
SDC_Agents turn your existing datastores into validated, signed SDC data in open formats (XML, RDF, JSON-LD) and deliver it into the store you already run, including your graph database or triplestore.
The meaning and the governance are bound to the payload and expressed in open standards, so they survive the platform. Swap the graph database, the LLM, or the hardware underneath without a replatform.
Running on your premises is not the same as being yours.
Data infrastructure was built for humans reading screens. Agents need something fundamentally different.
LLM agents probabilistically map your data in the dark. No constraints, no validation, no determinism. Every inference is a guess dressed up as an answer.
Governance bolted on via dashboards, not built into the data. When your audit trail lives outside the payload, compliance is a checkbox - not a guarantee.
IBM put the cost of bad data to the U.S. economy at $3.1 trillion a year. Data that cannot carry its own meaning is re-translated at every hop. Every integration is a new mapping project. Every mapping is a new failure point.
Built by researchers and developers with 26+ years of open source experience in semantic data modeling and health informatics across the US, Canada, Brazil, and the UK.
Meet the team →Define your data models in SDCStudio with mathematical constraints bound to the data. No ambiguity, no interpretation drift.
sdcvalidator and sdcgovernance enforce constraints at runtime. Every payload is checked against its model. Compliance is structural, not aspirational.
Run SDCStudio Sovereign on-premises for air-gapped environments, or use our cloud platform. Either way the meaning layer is yours, in open standards, and it stays yours if you ever leave.
Visual semantic data modeling platform. Build constraint-bound data models that agents can trust.
Air-gapped, on-premise deployment for regulated industries. Full control, zero cloud dependency.
API-first deterministic validation. Every data payload checked against its semantic model in real time.
See SDC in action. A working demonstration of semantic data exchange across agentic systems.
Open Distribution
SDC Agents is distributed in Google's official Agent Development Kit (ADK) community toolset, accessible to any developer building agents on the ADK Python framework.
Without deterministic validation, agents silently corrupt. Without payload-bound governance, compliance is a checkbox. Without semantic constraints, every system guesses differently.
Talk to us about making your data self-describing, your agents deterministic, and your compliance structural.
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