What is SAMOA™?
- Portfolio of Trustworthy AI Applications – SAMOA™ is a unified suite of AI applications designed to accelerate defense sustainment, engineering compliance, and operational readiness across the entire asset lifecycle.
- Ontology-First Knowledge Architecture – Uses semantic ontologies to model engineering concepts, relationships, and domain knowledge, enabling context-aware reasoning instead of document-based search.
- Evidence-Backed AI Decisions – Every recommendation is supported by Verifiable Claim Architecture (VCA), providing traceable evidence, citations, and transparent reasoning for engineering confidence.
- Human-Governed Decision Framework – The ERAD-H methodology (Evidence, Reasoning, Assumptions, Determination, Human) ensures that AI assists engineers while final authority always remains with qualified personnel.
- Specialized AI Applications – Includes capabilities for airworthiness compliance (CRG), engineering disposition (AACT), maintenance process automation (PEMS-AI), enterprise Data Fabric, and Ontology-Based Digital Thread.
- Enterprise Data Integration – Connects disconnected defense systems into a unified operational view, enabling real-time visibility, predictive insights, and cross-functional decision-making.
- Lifecycle Intelligence – Maintains continuity between Engineering, Manufacturing, and Sustainment Bills of Materials (EBOM, MBOM, SBOM) through a shared semantic ontology, creating a trusted digital thread across the lifecycle of military assets.
- Mission-Ready Outcomes – Reduces engineering bottlenecks, accelerates certification and maintenance workflows, preserves institutional knowledge, and improves fleet readiness through auditable, evidence-driven AI.
Understanding Shipcom's Trustworthy AI Architecture for Defense Sustainment
SAMOA™ (Shipcom Asset Management and Operational Availability) is a portfolio of trustworthy Artificial Intelligence applications engineered to accelerate defense sustainment, airworthiness compliance, maintenance engineering, and operational readiness.
Unlike conventional generative AI systems, SAMOA™ is designed around evidence-backed reasoning, ontology-driven knowledge models, and human-governed decision workflows to support mission-critical engineering decisions across the lifecycle of military assets.
Defense Sustainment Challenges
Military sustainment organizations face four fundamental technical challenges:
SAMOA™ Trustworthy AI Reference Architecture
Every application within SAMOA™ is built upon a common AI architecture consisting of three foundational technologies.
Ontology-First Knowledge Architecture
Rather than storing documents as isolated files, SAMOA™ organizes engineering knowledge using interconnected ontologies.Rather than storing documents as isolated files, SAMOA™ organizes engineering knowledge using interconnected ontologies.
The ontology models:
- Engineering concepts
- System relationships
- Component hierarchies
- Regulatory dependencies
- Maintenance procedures
- Configuration states
This semantic layer enables contextual reasoning instead of simple keyword retrieval. This semantic layer enables contextual reasoning instead of simple keyword retrieval.
Verifiable Claim Architecture (VCA)
Every AI recommendation generated by SAMOA™ includes:
Supporting evidence
Source documentation
Traceable citations
Explicit reasoning chain
Engineering justification
This architecture eliminates “black-box” AI responses by ensuring every conclusion can be audited and independently verified.This architecture eliminates “black-box” AI responses by ensuring every conclusion can be audited and independently verified.
ERAD-H Decision Workflow
SAMOA™ follows the ERAD-H engineering reasoning methodology. SAMOA™ follows the ERAD-H engineering reasoning methodology.
Evidence: Validated technical documentation, standards, maintenance records, and historical engineering cases.
Reasoning: Transparent logical evaluation connecting evidence to engineering conclusions.
Assumptions: Explicit declaration of any assumptions used during analysis.
Determination: Recommended engineering outcome supported by traceable documentation.
Human Authority: Final engineering approval always remains with qualified personnel.
Core Technology Modules
A simplified view of COVE consists of several integrated layers.
Airworthiness Auto-Compliance Engine (Compliance Report Generator - CRG)
This layer establishes trusted user identity before access is granted.
Automates Military Handbook 516C airworthiness certification for aircraft modifications.
- Military Handbook 516C compliance mapping
- Automated engineering evidence collection
- Certification document generation
- Airworthiness requirement validation
- Traceable compliance reporting
- KC-46 Pegasus
- C-130 Hercules
- C-17 Globemaster
- KC-135 Stratotanker
- C-5 Galaxy
Platform Environment
Runs on Platform One at Impact Level 5 (IL5), supporting controlled unclassified military information.
Engineering Disposition Intelligence Engine (AACT)
Accelerates Engineering Technical Assistance Request (ETAR) disposition generation for Air Force armament systems.
Combines keyword search with semantic similarity across:
- Technical Orders
- Military Standards
- Engineering documentation
Searches more than 800 validated engineering disposition cases to identify similar maintenance scenarios.
Produces:
- Relevant documentation
- Engineering rationale
- Section-level citations
- Comparable historical cases
within minutes instead of hours.
Process Effectiveness Management System with AI (PEMS-AI)
Automatically constructs validated maintenance workflows from multiple engineering data sources.
Process intelligence is structured around:
- Manning
- Method
- Machinery
- Material
- Measurement
- Measurement
- Medium
- Management
- Technical Orders
- Logistics Product Data (LPD)
- Interactive Electronic Technical Manuals (IETM)
- Quality Control Records
Generates a fully traceable Bill of Process (BOP) describing every maintenance activity required to complete depot work.
Artificial Intelligence Data Fabric
Creates a unified operational data layer across disconnected sustainment systems.
- Lean Depot Management System (LDMS)
- LIMS-EV
- Job Tracking Systems
- Backorder Databases
- CNC Machine Monitoring
- Engineering Operations
- Enterprise data integration
- Operational visualization
- Cross-system analytics
- Bottleneck identification
- Predictive sustainment monitoring
Ontology-Based Digital Thread
Maintains lifecycle continuity across engineering, manufacturing, and sustainment.
- Engineering Bill of Materials (EBOM): Captures original engineering intent.
- Manufacturing Bill of Materials (MBOM): Represents production configuration.
- Sustainment Bill of Materials (SBOM): Represents maintenance and operational configuration.
The ontology maintains relationships between all three BOMs, enabling trustworthy engineering queries throughout design, production, field operations, depot maintenance, and modernization.
Trustworthy AI Design Principles
SAMOA is engineered around five technical principles.
Operational Benefits
SAMOA™ enables defense organizations to:
- Accelerate airworthiness certification
- Reduce engineering disposition timelines
- Automate maintenance process engineering
- Connect fragmented enterprise systems
- Preserve institutional engineering knowledge
- Improve fleet readiness
- Increase engineering traceability
- Support regulatory and audit compliance
- Deliver evidence-backed AI recommendations suitable for mission-critical operations