Git-Native, AI-Native MBSE

Reqvire brings Model-Based Systems Engineering into the Git workflow as a semantic engineering graph for building verifiable and traceable systems. Instead of keeping system models in a separate tool and requirements in separate documents, Reqvire stores the semantic engineering graph as structured Markdown that can be reviewed, validated, queried, and used by AI assistants.

The model is not just a requirements list. It is an engineering knowledge graph connecting:

  • ontologies
  • capabilities
  • requirements
  • refinements
  • verification evidence
  • implementation artifacts

Where Reqvire Fits

Reqvire is converging at the intersection of:

  • SysML and MBSE
  • knowledge graphs
  • ontology-driven engineering
  • semantic engineering
  • context engineering
  • AI-native development infrastructure

It keeps MBSE traceability and lifecycle discipline, but represents the model in a Git-native form that software teams and AI systems can both use.

What MBSE Means Here

Model-Based Systems Engineering shifts engineering from static documents toward connected models of system meaning, behavior, obligations, interfaces, verification, and evidence.

In Reqvire, that model remains lightweight and Git-native:

  • Ontologies define reusable domain meaning.
  • Capabilities define stable operational or system abilities.
  • Requirements define implementable obligations.
  • Refinements capture behavioral, state, semantic, input/output, constraint, and specification detail.
  • Verifications prove that obligations and capability expectations are met.
  • Implementation artifacts show where requirements and evidence are realized.

This makes the model useful to systems engineers, software engineers, reviewers, compliance stakeholders, and AI systems.

Why Capabilities Matter

Capabilities are the semantic bridge between domain meaning and implementable work. They describe what the system is able to accomplish without locking the model to a UI screen, deployment artifact, ticket, or code module.

Good capabilities are stable, decomposable, implementation-independent, and verifiable. They provide durable traceability anchors for requirement clusters, ontology context, verification evidence, and architecture impact.

AI-Native Context

Reqvire models are intentionally readable by both humans and AI systems. The graph gives assistants structured context for:

  • finding relevant requirements before editing code
  • understanding capability intent
  • resolving ontology vocabulary
  • checking verification and implementation evidence
  • generating implementation tasks
  • explaining change impact
  • maintaining traceability during refactors

AI can work from the same versioned model that engineers review in pull requests.

Typical Workflow

  1. Define or refine capability intent.
  2. Attach ontology context that gives the capability stable domain meaning.
  3. Derive requirements that specify the capability.
  4. Add refinements for behavior, state, constraints, I/O, semantic contracts, or detailed specifications.
  5. Link requirements and capabilities to verification elements.
  6. Link requirements and evidence-backed verifications to implementation artifacts.
  7. Run validation, coverage, traces, and change impact before review.

Benefits

  • Less drift: requirements, semantic meaning, verification, and implementation evidence stay linked.
  • Better reviews: pull requests can show impact and coverage in the same repository as the code.
  • Better AI context: assistants reason from a structured graph instead of scattered prose.
  • Better lifecycle traceability: changes propagate through capabilities, requirements, verifications, and artifacts.
  • Lower process overhead: the model stays in Markdown and Git rather than a separate heavyweight tool.

Reqvire makes MBSE a practical, reviewable, AI-ready extension of everyday engineering work.