Meaning fragments across systems.
Teams inherit conflicting models, vocabulary, and assumptions. The same concept changes meaning as it crosses tools and domains.
Decision intelligence · Semantic systems · Private AI
Make complex systems intelligible.
Cardamom Cosmos turns fragmented business and technical knowledge into decision-ready evidence—for people and AI systems that must preserve context, provenance, and control.
Sensitive context requires explicit boundaries—not blanket access.
The operating problem
Where enterprise intelligence breaks
Evidence disappears behind outputs.
Answers arrive without enough lineage to test what shaped them, what disagreed, or what changed.
Sensitive context crosses boundaries.
AI is most useful when it has context—but that context needs explicit limits, traceable use, and appropriate control.
What Cardamom Cosmos does
From fragmented knowledge to defensible action.
Four connected capabilities help technical and business leaders reason across systems without turning the underlying method into a black box.
Decision & knowledge intelligence
Organize research, operating knowledge, and evidence into queryable structures that reveal relationships, disagreement, and change.
- Evidence with lineage
- Cross-source reasoning
Semantic interoperability
Align domain models and vocabularies while keeping authority, provenance, and human review visible.
- Model alignment
- Traceable interpretation
AI engineering assurance
Give AI-assisted development structural context: dependencies, change impact, and tests that carry the risk.
- Change-aware context
- Evidence-led review
Private AI architecture
Design data and execution boundaries around what agents may use, under which conditions, and what evidence they must return.
- Explicit data boundaries
- Traceable agent action
A bounded engagement
Start with the decision—not the platform.
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01
Frame the decision
Name the consequential question, its owners, and what credible evidence must support the answer.
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02
Structure evidence and boundaries
Map meaning, provenance, disagreement, and the controls required for people and AI to work safely.
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03
Deliver a bounded pilot
Test a narrowly defined capability against agreed evidence and review criteria before expanding scope.
Public evidence
Work you can inspect.
These links provide public context for leadership, public work, and stated experience. They do not imply endorsement by the organizations or platforms named.
Leadership
Network Architecture · W3C Holon Community Group
Brian Boyd was asked to chair the group's Network Architecture / API working team.
Explore the public specification work Public work Semantic interoperability in NIEMOpenA public ontology-alignment pipeline whose repository provenance names earlier CardamomCosmos work.
Inspect the repository Experience 20+ years in cloud solutions architectureBrian Boyd's public speaker profile describes his experience and security-first approach.
View Brian's profileCardamom Cosmos R&D
Privacy-first agentic systems
Exploring protected execution models where private context can support AI without becoming default platform exhaust.
The research focus is user-controlled data boundaries, traceable agent actions, and private-by-design compute. Architecture and internal research remain private; this is a direction of inquiry, not a claim of a completed product.
Start narrowly
Bring one difficult decision.
If fragmented knowledge, model mismatch, change risk, or private context is keeping a consequential question unresolved, let's define a bounded first conversation.
Discuss the problem