Fire Force VI Digital Garden

Systems Engineering CoPilot

7 min read

AI-Powered Systems Engineering Assistant

Overview

FireForce VI’s complexity spans multiple domains, work packages, and thousands of engineering artifacts. Engineers need instant access to project knowledge and assistance with complex systems engineering workflows. This work package delivers an intelligent AI assistant that understands the entire FireForce VI ecosystem, answers questions in natural language, and actively assists with systems engineering tasks through pre-configured workflows integrated directly into development environments. It provides context-aware assistance for the Modeling Environment, Project Environment, and Testing Environment, with deep understanding of the SoS Architecture and all system components.

The Challenge

Modern systems engineering projects face critical knowledge management challenges:

  • Information scattered across repositories, documents, models, and specifications
  • Engineers spending hours searching for relevant context
  • Steep learning curve for new team members
  • Complex workflows requiring deep domain expertise
  • Difficulty maintaining consistency across work packages
  • Time-consuming manual tasks that could be automated
  • Loss of institutional knowledge during team transitions

Traditional search tools and documentation systems cannot understand semantic relationships, reason about system architecture, or actively assist with engineering workflows.

Core Capabilities

1. Configurable Context Management

Intelligent Repository Indexing

  • Automatic ingestion of Git repositories with version awareness
  • Support for multiple content types (code, models, documentation, diagrams)
  • Incremental updates on repository changes
  • Transitive dependency resolution and indexing
  • Release-specific context configuration
  • Cross-repository relationship mapping

Semantic Understanding

  • Natural language processing of technical documentation
  • OML model comprehension and reasoning
  • Code structure and API understanding
  • Requirements traceability extraction
  • Architectural relationship inference
  • Domain-specific terminology learning

Context Switching

  • Quick switching between work package contexts
  • Multi-package queries spanning dependencies
  • Historical version comparison
  • Baseline and release context management
  • Personal context preferences
  • Team-shared context configurations

2. Natural Language Query Interface

Conversational AI

  • Context-aware natural language understanding
  • Multi-turn conversations with memory
  • Clarifying questions for ambiguous queries
  • Follow-up question suggestions
  • Technical terminology handling
  • Code and model snippet generation

Intelligent Retrieval

  • Semantic search across all project artifacts
  • Ranked results by relevance
  • Source attribution with direct links
  • Visual preview of referenced content
  • Related information suggestions
  • Citation and traceability chains

Query Types

  • Factual: “What is the latency requirement for drone communication?”
  • Analytical: “What components depend on the Fire Cloud API?”
  • Procedural: “How do I add a new sensor to the Scout Drone?”
  • Comparative: “What’s the difference between v2.0 and v2.1 of the Fire Warden?”
  • Diagnostic: “Why is the integration test failing for Mission Control?”
  • Generative: “Generate a test plan for the Tanker Drone autonomous mode”

3. Pre-Configured SE Workflows

Requirements Engineering

  • Requirements extraction from stakeholder input
  • Requirements template generation
  • Traceability matrix creation
  • Requirement validation and consistency checking
  • Change impact analysis
  • Test case generation from requirements

Architecture Design

  • Component interface generation
  • Architecture pattern recommendations
  • Design consistency validation
  • Integration point identification
  • Technology stack suggestions
  • Documentation generation

Model Engineering

  • OML model scaffolding
  • Vocabulary extension suggestions
  • Relationship inference
  • Model validation and repair
  • Diagram generation
  • Model refactoring assistance

Testing & Validation

  • Test scenario generation
  • Test data creation
  • Coverage analysis
  • Bug report summarization
  • Root cause analysis assistance
  • Regression test selection

Documentation

  • Technical documentation generation
  • API documentation extraction
  • User guide creation
  • Diagram explanations
  • Change log generation
  • Release notes compilation

4. Modeling Environment Integration

Direct Model Editing

  • In-editor AI suggestions
  • Auto-completion for OML constructs
  • Inline documentation generation
  • Refactoring recommendations
  • Error explanation and fixes
  • Pattern-based code generation

Visual Assistance

  • Diagram generation from descriptions
  • Visual query results
  • Interactive exploration guides
  • Annotation and explanation overlays
  • Tutorial walkthroughs
  • Visual diff explanations

Workflow Orchestration

  • Step-by-step guided workflows
  • Automated task execution
  • Progress tracking and checkpoints
  • Validation at each step
  • Rollback capabilities
  • Success criteria verification

Technical Architecture

AI/ML Stack

  • Language Model: GPT-4 or fine-tuned open-source LLM (Llama, Mistral)
  • Embedding Model: Specialized technical embedding (CodeBERT, GraphCodeBERT)
  • Reasoning Engine: Symbolic reasoning for OML models
  • Vector Database: Pinecone, Weaviate, or Milvus
  • Knowledge Graph: Neo4j for relationship modeling
  • Fine-tuning Pipeline: Custom domain adaptation

Backend Services

  • API Gateway: FastAPI with async support
  • Context Processor: Python-based ingestion pipeline
  • Query Engine: Hybrid search (semantic + keyword)
  • Workflow Engine: State machine for guided workflows
  • Integration Hub: Connectors for dev environments
  • Monitoring: LLM output quality and performance tracking

Frontend Integration

  • Chat Interface: React-based conversational UI
  • Editor Plugin: VS Code/Monaco extension
  • Web Portal: Standalone query interface
  • CLI Tool: Terminal-based assistant
  • IDE Extensions: JetBrains, Eclipse plugins
  • API Client: RESTful and GraphQL interfaces

Success Metrics

MetricTargetMeasurement Method
Query Accuracy≥95% correct answersHuman evaluation + automated testing
Workflow Completion≥80% success rateWorkflow telemetry tracking
Context Precision≥90% relevant retrievalPrecision@K evaluation
User Adoption≥70% weekly active usersUsage analytics
Response Time<3 seconds averagePerformance monitoring
User Satisfaction>4.5/5 ratingUser surveys

Technical Challenges

1. Context Ingestion & Semantic Understanding

  • Challenge: Processing and understanding diverse technical artifacts (code, models, diagrams, specs) while maintaining semantic relationships and domain-specific meaning
  • Approach: Multi-modal embedding strategies, custom fine-tuning for systems engineering, ontology-aware processing, incremental learning from user feedback
  • Skills Required: NLP, multi-modal ML, knowledge representation, domain modeling

2. Multi-Modal Integration

  • Challenge: Seamlessly integrating AI assistance across different development environments (web, IDE, CLI) while maintaining consistent experience and context
  • Approach: Platform-agnostic API design, unified context management, plugin architecture, shared state synchronization, responsive UI adaptation
  • Skills Required: Cross-platform development, API design, plugin systems, state management

3. Real-Time Knowledge Graph Updates

  • Challenge: Maintaining up-to-date knowledge graph as repositories change, handling concurrent updates, and propagating changes across dependent contexts
  • Approach: Event-driven architecture with webhook integration, incremental graph updates, version-aware indexing, conflict resolution strategies
  • Skills Required: Graph databases, event-driven systems, distributed systems, concurrency control

4. Domain-Specific Reasoning

  • Challenge: Going beyond generic LLM capabilities to provide systems engineering-specific reasoning about architecture, requirements, traceability, and validation
  • Approach: Hybrid AI combining LLMs with symbolic reasoning, custom fine-tuning on SE corpus, rule-based validation layers, domain-specific prompt engineering
  • Skills Required: Knowledge representation, symbolic AI, ontological reasoning, domain expertise

Team Structure

Required Roles

  • ML/NLP Engineer
  • Knowledge Systems Engineer
  • Full-Stack Developer
  • UI/UX Designer
  • Optional: DevOps/MLOps Engineer

Deliverables

  1. AI Query Engine - Natural language interface with high accuracy
  2. Context Management System - Repository indexing and version control
  3. Workflow Assistant - Pre-configured SE workflows with UI
  4. IDE Integrations - Plugins for VS Code and web-based editors
  5. Knowledge Graph - Semantic representation of project relationships
  6. Analytics Dashboard - Usage metrics and quality monitoring
  7. Documentation - User guides, API docs, workflow templates

Technology Stack Recommendations

  • LLM: OpenAI GPT-4 API or self-hosted Llama/Mistral
  • Embeddings: sentence-transformers, CodeBERT
  • Vector DB: Pinecone (cloud) or Milvus (self-hosted)
  • Graph DB: Neo4j or Amazon Neptune
  • Backend: Python FastAPI, LangChain framework
  • Frontend: React, TypeScript, WebSocket
  • Deployment: Docker, Kubernetes, GPU support
  • Monitoring: Weights & Biases, Prometheus, Grafana

Integration Points

Example Use Cases

Onboarding New Team Member

Engineer: "I'm new to the Scout Drone package. What does it do?"
CoPilot: "The Scout Drone is a reconnaissance platform that provides 
real-time fire monitoring. It integrates with Fire Cloud for data 
transmission and uses thermal sensors for detection. Let me show you 
the architecture diagram and key interfaces..."

Debugging Integration Issue

Engineer: "Why is the Mission Control unable to receive telemetry from drones?"
CoPilot: "Analyzing the integration logs and models... The issue appears 
to be a version mismatch in the telemetry protocol. Mission Control expects 
v2.1 but drones are using v2.0. Here's the compatibility matrix and suggested fix..."

Creating New Component

Engineer: "Help me add a new sensor type to the Tanker Drone"
CoPilot: "I'll guide you through this workflow:
1. Define sensor interface in OML model
2. Update component architecture
3. Modify communication protocol
4. Add test scenarios
Let's start with step 1. Here's a template based on existing sensors..."

Learning Outcomes

Participants will gain expertise in:

  • Large language model application development
  • Retrieval-augmented generation (RAG) systems
  • Knowledge graph engineering
  • Multi-modal AI systems
  • Conversational AI interface design
  • Domain-specific AI fine-tuning
  • MLOps and model deployment
  • Human-AI interaction design

Industry Relevance: This technology mirrors advanced AI assistants used in aerospace (Boeing’s engineering AI), defense (Palantir’s AIP), and tech companies (GitHub Copilot, Cursor), adapted specifically for systems engineering workflows and ontological reasoning.

Future Enhancements

  • Proactive Suggestions: Identify potential issues before they occur
  • Collaborative Learning: Learn from team interactions and preferences
  • Multi-Agent Systems: Specialized agents for different SE domains
  • Visual Understanding: Process and reason about diagrams and schematics
  • Code Generation: Full component implementation from specifications
  • Automated Testing: Generate and execute comprehensive test suites

Graph View

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