Dependency clarity
Directed graphs and trace links so "what depends on what" is inspectable, not guessed from slide decks.
Systems engineering · Services
Requirements that come from your data, not from thin air
We help engineering programs put geometry, budgets and requirements into one model that can be checked continuously. Mass, power, clearance, harness and sensor coverage become measured numbers rather than spreadsheet entries, and a budget requirement becomes an executable constraint that reports by how much it was missed. The review pack comes out of the same pass the engineer was looking at.
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, semantic similarity 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. A model is plain JSON in SI units; MBE3Dstudio is the studio and analysis pass around it. It adds geometry, budgets, routed harness and executable constraints on top.
- Model Characterization Pattern. The INCOSE community practice for recording trust and lifecycle for computational models. It is orthogonal to MBE3Dstudio but sits naturally alongside a model whose numbers are all derived.
- A project is a file. There is no server to stand up and no state trapped in a service. Details are on Deploy.
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
MBE3Dstudio adoption
Ontology design, hybrid symbolic-vector reasoning (tunable α), and semantic indexing tied to engineering nodes so similarity search stays grounded in your graph.
- OWL domains, constraints, and reviewable ontology evolution
- Kernel-style similarity over telemetry, simulation result metadata, and document corpora where appropriate
- Roadmap alignment with the published roadmap
MCP integration & assistant-ready context
Get MBE3Dstudio running where your engineers already work (hosted, behind your own web server, or fully offline), so the model, the review pack and the bill of materials all come out of one place.
- 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.
- Constraint 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. It is orthogonal to the MCP protocol but storable as structured ontology and evidence where your program demands it.
- Mapping characterization records against the model that produced the numbers
- Linkage from constraints to verification evidence and the requirement tags they trace to
Applied research & interactive prototypes
Build-to-learn: dependency and scenario lenses, notebooks, reproducible datasets, and demos that make structure tangible for leadership. All research builds are labeled 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 versus research artifacts based on 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 the analysis pass, the model schema, 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.