From raw model data to validated requirements

Extract functions with inputs and outputs

VectorOWL scans the graph for all function-type nodes and resolves their typed interfaces. Input parameters, output parameters, and flow ports are extracted and stored as structured edges. The result is a queryable function catalog with full interface visibility.

Extract signal enums and bitwise data

Signal definitions, enumeration types, and bitfield layouts are parsed and attached to their function and component nodes as typed attributes. CAN/LIN DBC and ARXML signal catalogs are read directly. Each signal gets a unique URI so requirements and anchors can reference it without ambiguity.

Extract spec limits for boundary conditions

Operating ranges, tolerance bands, and hard limits from your uploaded documents and model annotations are extracted and registered as candidate anchor constraints. Each limit is linked to the signal or function it governs so the boundary condition is traceable to its source document or standard.

Generate requirements

With the graph populated, open the requirements generator. Optionally upload a specification document for additional context, then describe what you need in plain language. The AI reads your functions, signals, and existing requirements and produces formally structured candidates. Select the ones you want and add them to the requirements table in one click.

Check requirements allocation

Every requirement must be allocated to at least one function, component, or system node. The traceability matrix shows which requirements are unallocated and which components have no requirement coverage. Fix gaps by drawing allocation edges directly in the graph or from the detail panel.

Validate requirements and export

The AI quality checker inspects each requirement against a set of engineering rules: ambiguous terms, weak verbs, missing verification methods, missing rationale, and duplicate detection. Issues are ranked by severity. Apply suggested fixes inline without leaving the table. Each change is logged and versioned in the graph.

Once requirements are validated, export them to downstream tools. Use Export as CSV for spreadsheet workflows, or Export as ReqIF to import directly into DOORS, Jama, PTC Integrity, or any ReqIF 1.0-compatible tool. The export includes all attribute fields: ID, text, type, status, priority, risk, verification method, allocation, owner, rationale, source, and tags.

Simulate requirements

Push validated requirements into the simulation layer via MCP. VectorOWL compares requirement bounds against outputs from your connected simulation tools (CFD, FEA, co-sim, or surrogate models). Anchor constraints from Step 4 are enforced as gates: a simulation result that violates an anchor is flagged before it can be merged into the graph.

Parametrics and trade studies with MCP and AI

With a validated, evidence-backed graph, use MCP-connected AI agents to sweep design parameters and compare alternatives. Each trade study run queries the graph for constraints, executes the parametric sweep, and logs results with full provenance: which requirement drove which bound, which model produced which number. Agents cannot override anchor gates, so the governed graph stays consistent throughout.