Aerospace & Defence

Bud MBSE Co-Pilot

AI-Powered Systems Modeling

Transform requirements documents, engineering diagrams, and natural language instructions into validated SysML v2 models with full traceability. Deployed entirely on-premise with zero external data dependency.

9 Specialist Agents

100% Syntax Validity

6 MBSE Tool Integrations

0 External Dependencies

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The Challenge

Why MBSE Adoption Stalls

Model-Based Systems Engineering adoption in aerospace remains limited despite decades of advocacy. The barriers are structural, not aspirational.

Manual Extraction

Requirements pulled from PDFs by hand, taking weeks per document with significant error rates.

Diagram Graveyard

Visio, PowerPoint, and whiteboard diagrams cannot be automatically ingested into formal MBSE tools.

Expertise Barrier

SysML proficiency requires years of training. Months to onboard new systems engineers.

Broken Traceability

Manual trace links break under every change, causing audit failures and costly rework.

No Compliance Check

DO-178C, ARP4754A verified manually, leading to late-stage certification surprises.

Siloed Tools

Capella, Cameo, DOORS don't communicate natively, requiring manual copy-paste integration.

The Solution

AI-Augmented MBSE at Sovereign Scale

Bud MBSE Co-Pilot is a sovereign, multi-agent AI system that enables aerospace and defence organizations to perform end-to-end Model-Based Systems Engineering using natural language, vision, and structured tooling.

Natural language to validated SysML v2 with ANTLR grammar validation

Vision-based diagram parsing for legacy archives

Automated requirements extraction from PDF, Word, and images

Automated standards compliance checking (DO-178C, ARP4754A, ECSS, MIL-STD-882E)

Bidirectional traceability management with impact analysis

Multi-tool integration via MCP servers

Configurable human-in-the-loop review gates for safety-critical systems

Sovereign air-gapped deployment on Bud Pod infrastructure

AI Extraction

LLM + VLM extract and classify requirements in minutes, not weeks.

VLM Diagram Parsing

Upload any diagram and get a formal model. Legacy archives unlocked.

Natural Language Interface

Describe in English, get validated SysML v2 with 100% syntax validity.

Unified MCP Layer

Capella, Cameo, DOORS via one protocol. Tool-agnostic agents.

Before & After

Measurable Transformation

2-4 weeks

8-12 minutes

Model Creation

~60% valid

100% valid

Syntax Validity

Manual traces

Auto + HITL

Traceability

1 tool only

6 tools via MCP

Tool Coverage

Sovereign Air-gapped Zero external dependency

System Architecture

Five-Layer Design

A hierarchical multi-agent system with clear responsibility boundaries, well-defined interfaces, and independent scalability.

Layer 5

Bud Studio

NL chat, diagram upload, model browser, review, compliance

Layer 4

PEVR-DAG Orchestration + AgentScript v2

Planner Executor Validator HITL Gateway Reflector

Layer 3

Specialist Agents

ReqsExtract DiagParse ModelGen Validate TraceMap Assess StdComply TradeOff DocGen

Layer 2

Knowledge and Retrieval (GRAG)

GRAG Engine SysML v2 KG Standards Templates Cognee Memory

Layer 1

MCP Tool Integration (Bud MCP Foundry)

Capella Cameo Rhapsody SysML v2 API DOORS Simulink

Layer 0

Bud AI Foundry (Sovereign Infrastructure)

Bud LayerZero Model Foundry Bud Sentinel Bud Pod

Nine Specialist Agents

Purpose-Built AI for Systems Engineering

Each agent is a self-contained unit with defined inputs, outputs, and access to specific knowledge stores.

Requirements Extraction

Uses LLM + VLM to identify, classify, and structure requirements from PDFs, Word documents, images, and regulatory standards. Applies INCOSE writing rules.

Output: SysML v2 requirement definitions with source traceability

Diagram Parsing

Vision-language model extracts shapes, text labels, spatial relationships, and flow directions from architecture diagrams. Supports hand-drawn, Visio, PowerPoint, and photographs.

Output: Intermediate graph representation with confidence scores

Model Generation

Core generation pipeline: specification extraction, template retrieval, GRAG context, SysML v2 generation, and ANTLR validation with self-correction loop.

Output: 100% syntactically valid SysML v2 models

Validation

Four-tier validation: syntactic (ANTLR grammar), semantic (constraint checking), completeness (requirement allocation), and consistency (no dangling references).

Output: Validation report with actionable findings

Traceability Mapping

Generates and maintains bidirectional trace links across the full V-model. Performs real-time impact analysis when any model element is modified.

Output: Bidirectional trace matrix with impact analysis

Assessment

Architecture pattern detection, quality metrics computation, coupling/cohesion analysis, and design review preparation using reasoning-class LLMs.

Output: Quality assessment report with recommendations

Standards Compliance

Automated compliance checking against aerospace standards: DO-178C, ARP4754A, ARP4761, ECSS, MIL-STD-882E. Evidence mapping and gap analysis.

Output: Compliance matrix with evidence mapping

Trade-Off Analysis

Multi-objective optimization with deep reinforcement learning, Pareto frontier analysis. Integrates with Simulink for simulation-based evaluation.

Output: Design alternatives with trade-off visualization

Document Generation

Generates formatted documents from model content: System Specification, Interface Control Document, V&V Plan, Compliance Matrix, Design Justification.

Output: Publication-ready technical documents

Knowledge Retrieval

Graph Retrieval-Augmented Generation

Unlike naive RAG that loses relationships when chunking, GRAG retrieves textual subgraphs that preserve the topological relationships between requirements, components, interfaces, and constraints.

This enables the system to answer questions like "What is the impact of changing requirement REQ-042 on the thermal subsystem?" through graph traversal rather than text similarity.

Knowledge Graph

Neo4j with APOC + GDS for multi-hop reasoning and traceability

SysML v2 Templates

50+ aerospace domain models for few-shot generation

Standards Store

Vector DB with DO-178C, ARP4754A, ECSS, MIL-STD-882E

Pattern Library

Redundancy, watchdog, sensor fusion, command patterns

Memory Engine

Session history, user corrections, organizational conventions

Competitive Advantage

What Sets Bud MBSE Co-Pilot Apart

Sovereign Deployment

Air-gapped operation on Bud Pod with zero external dependency. All AI inference runs locally with no data leaving the deployment boundary.

MCP-Toolized Integration

All MBSE tools exposed via MCP servers for tool-agnostic orchestration. Capella, Cameo, Rhapsody, DOORS, Simulink unified under one protocol.

Full Infrastructure Stack

Complete infrastructure from GPU orchestration to agent runtime. Production-grade, not research prototype.

AgentScript v2

Structured, auditable control flow meeting aerospace compliance requirements. Full observability with immutable audit trail.

Self-Correction Loop

ANTLR validation with up to 3 self-correction iterations guarantees 100% syntactic validity for all generated models.

Human-in-the-Loop

Configurable HITL checkpoints with side-by-side comparison, one-click approve/reject/modify, and batch review capability for safety-critical systems.

Multi-Tool Integration

Unified Access to Industry-Standard MBSE Tools

Every MBSE tool interaction is wrapped in a Model Context Protocol (MCP) server exposing standardized tools for tool-agnostic orchestration.

Capella

create_element, modify_element, query_model, export_diagram

Python4Capella API

Cameo

create_bdd, create_ibd, add_requirement, run_validation

Cameo REST API

Rhapsody

create_model, add_block, set_property, link_trace

IBM ELM REST API

SysML v2 API

create_package, create_part_def, validate_syntax

OMG SysML v2 API

DOORS

import_requirements, create_trace_link, export_module

DOORS Next REST API

Simulink

run_simulation, extract_parameters, set_conditions

MATLAB Engine API

Ready to Transform Your Systems Engineering?

Discover how Bud MBSE Co-Pilot can accelerate MBSE adoption in your aerospace and defence programs with sovereign, AI-augmented modeling.

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