Dependency clarity
Directed graphs and trace links so “what depends on what” is inspectable—not guessed from slide decks.
agent-ready MBSE · Services
Engagements built on VectorOWL + MCP
We help engineering organizations adopt hybrid reasoning MBSE: OWL-backed graphs for what must be logically true, vector retrieval over simulations and documents where meaning matters, Model Context Protocol (vectorowl-mcp) so hosts and tools read the same graph, anchors where soft inference cannot override review obligations, and hooks for computational-model characterization—the same substrate described on our homepage.
The vision
The feedback-driven engineering substrate
The transition from model-driven to feedback-driven engineering is where MBSE must deliver for teams that use AI: the system evolves as data lands, under rules you can audit. Coding agents often run without a shared structural spine—or consistent computational-model trust metadata. Our services close that gap: semantics-first modeling in Git, embeddings where axioms alone are not enough, and MCP so specifications, CAD/CAE-style tools, and assistants stay aligned as branches and merges move.
What these services assume (plain English)
- Not a new language. You express the system in OWL; VectorOWL is the framework around that graph—plus vectors, MCP wiring, and anchors.
- Two “MCP” phrases.Model Context Protocol = how hosts load
vectorowl-mcp. Model Characterization Pattern (INCOSE community) = trust and lifecycle records for computational models. We support both meanings—see model characterization in the framework. - Skills vs server. Optional
SKILL.mdfiles teach vocabulary; the MCP server is the integration surface—details on MBSE & install.
Why hire us
When the system is tangled, spreadsheets fail first
The bottleneck is rarely raw data. It is knowing how parts depend on one another, and defending what happens when a constraint moves upstream. Engagements translate that pressure into reviewable structure: graphs you can query, retrieval you can attribute, and integration patterns that do not fracture under automation.
Defensible outputs
Scenarios and recommendations tied to inputs, rules, and provenance—aligned to engineering and audit habits.
One graph, many actors
Human reviewers, CI gates, and MCP-connected tools consume the same versioned model—fewer contradictory “sources of truth.”
Offerings
How we work with your team
Engagements combine advisory, modeling support, integration architecture, and research prototypes—scoped to your assurance level. Production hardening follows your governance; we label research builds honestly.
MBSE alignment & traceability
Structure requirements, architecture, behavior, and verification so intent survives scale and turnover. Emphasis on version-aligned change, impact visibility, and V&V posture—including records that support release gates.
- Trace patterns: specify, satisfy, verify; change signals when upstream assumptions move
- Branch/merge-aware modeling workflows alongside your Git practice
- Coordination with computational-model trust metadata where programs require it
VectorOWL framework adoption
Ontology design, hybrid symbolic–vector reasoning (tunable α), and embedding pipelines tied to engineering nodes—so similarity search stays grounded in your graph.
- OWL domains, constraints, and reviewable ontology evolution
- Kernel-style similarity over CFD, FEA, telemetry, and document corpora—where appropriate
- Roadmap alignment with the open framework overview
MCP integration & assistant-ready context
Package vectorowl-mcp for your hosts: tool surfaces, dataset status, and context bundles so coding agents and automation pull from the same graph your engineers review—not from unmanaged prompts alone.
- MCP server layout, secrets, and environment patterns for your toolchain
- CI hooks and human-gated promotion aligned to your policies
- Integration posture for CAD/CAE/PLM-adjacent workflows where applicable
Anchors, constraints & safety posture
Deterministic predicates and logs where probabilistic inference must not override obligations: safety limits, policy gates, and audit-friendly enforcement points.
- Anchor design against your hazard and assurance vocabulary
- Solver and logging patterns suitable for review and replay
- Clear separation between soft retrieval and hard must-never-break rules
Computational model characterization
Align with community practice for describing trust and lifecycle for computational models—orthogonal to the MCP protocol, but storable as structured ontology and evidence where your program demands it.
- Mapping characterization records into your VectorOWL-backed knowledge base
- Linkage to verification evidence and downstream agents via MCP-aware workflows
Applied research & interactive prototypes
Build-to-learn: dependency and scenario lenses, notebooks, reproducible datasets, and demos that make structure tangible for leadership—explicitly labeled when non-operational.
- Graph and map exploration UIs; exports (GeoJSON, JSON) with documented schemas
- Scenario workflows you can replay; performance guards for large graphs
Typical engagement flow
- Align on program context, assurance bar, and toolchain (Git, hosts, CAD/CAE boundaries).
- Assert structure and constraints in the ontology-backed model; define anchor policies where needed.
- Bring simulations, telemetry, and documents into the vector layer tied to nodes.
- Stand up MCP paths so assistants and automation query governed context.
- Iterate with traceability visible in review—scope hardening vs research artifacts to match your risk posture.
Approach
Provenance first, accountable delivery
We label sources, assumptions, and uncertainty so outputs can be reviewed—not laundered through prose. Research prototypes are positioned honestly relative to operational verification requirements. Depth lives in VectorOWL technical material, the framework story, and the product narrative on the homepage.
Research prototype; not investment advice. Hosted demos may be offline during maintenance—same caveat as our home hero.