Enterprise MoE Architecture using World-Model Distillation.pdf

This paper describes an Enterprise AI Mixture of Experts Architecture where the AI domain experts are organized around departments. A large generic Generative AI package is used to train AI domain experts by distillation include the transmission of departmental world-models for initial training. The generic AI package also handles prompts that can be routed to departmental experts. The confidence-based routing algorithm is based on self description of AI departmental experts

1. World-Model Distillation for Enterprise
Departmental AI Systems: A Mixture-of-
Experts Architecture with Confidence-Based
Routing
Abstract
Enterprise organizations increasingly seek to deploy generative AI systems that leverage internal
data while maintaining data privacy and security. We propose a departmentally-aligned mixture-
of-experts (MoE) architecture that combines world-model distillation with organizational
structure to create specialized AI systems for enterprise use. Our approach utilizes a large
generalist foundation model to generate department-specific world models, which are then used
to train smaller, efficient departmental experts through structured knowledge transfer. A
confidence-threshold router with expert self-description coordinates query routing across
departments while maintaining conservative fallback mechanisms. This architecture addresses
key enterprise requirements including data privacy, computational efficiency, domain
specialization, and organizational alignment while providing clear governance and maintenance
pathways. We detail the technical implementation, discuss organizational challenges, and provide
evaluation frameworks for measuring system effectiveness.
Keywords: artificial intelligence, mixture of experts, knowledge distillation, enterprise AI,
departmental specialization, routing systems
1. Introduction
Enterprise adoption of generative AI faces fundamental challenges in balancing capability,
efficiency, and governance. Large language models (LLMs) demonstrate remarkable
generalization but require significant computational resources and raise data privacy concerns
when used with external services. Small, specialized models offer efficiency and privacy but lack
the breadth needed for diverse organizational queries.
Recent work by NIST and other organizations has demonstrated the viability of retrieval-
augmented generation (RAG) systems for internal document search and knowledge access.
However, enterprises typically organize knowledge and expertise across multiple departments
with distinct data types, procedures, and domain requirements. A monolithic AI system, even
with RAG capabilities, may not effectively capture the specialized knowledge and workflows
that exist within departmental boundaries.
This paper introduces a departmentally-aligned mixture-of-experts architecture that leverages
world-model distillation to create specialized AI systems for enterprise deployment. Our key
contributions include:
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2. 1.Departmental MoE Architecture: A system design that aligns AI specialization with
organizational structure
2.World-Model Distillation Protocol: A method for transferring structured knowledge
from generalist models to departmental experts
3.Confidence-Threshold Routing: A practical approach to query routing with expert self-
description and conservative fallback
4.Enterprise Implementation Framework: Practical considerations for organizational
deployment and governance
2. Related Work
2.1 Mixture-of-Experts Systems
Mixture-of-experts architectures have demonstrated effectiveness in scaling neural networks
while maintaining computational efficiency. Traditional MoE systems focus on parameter
efficiency and scaling, while recent work has explored domain-specific specialization and
knowledge transfer approaches.
2.2 Knowledge Distillation
Model distillation techniques typically transfer task-level performance from teacher to student
models. However, these approaches often neglect the structured knowledge representations that
could enable more interpretable and maintainable specialized systems.
2.3 Enterprise AI Deployment
Organizations like NIST have demonstrated practical approaches to deploying AI systems for
internal knowledge access, highlighting key requirements around data privacy, security, and
domain specificity. These implementations provide valuable insights into real-world deployment
challenges and solutions.
3. Enterprise Departmental MoE Architecture
3.1 System Overview
Our proposed architecture consists of four primary components:
Large Generalist Foundation Model: Serves as the source of world-model knowledge and
handles queries that span multiple departments or fall outside departmental expertise.
Departmental Domain Experts: Smaller, specialized models trained through world-model
distillation to handle department-specific queries with high efficiency and accuracy.
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3. Confidence-Threshold Router: A routing system that classifies queries and directs them to
appropriate departmental experts or the generalist based on confidence estimates and capability
matching.
World-Model Generator: A specialized component that extracts department-specific structured
knowledge from the generalist model to bootstrap departmental expert training.
3.2 Departmental Alignment Rationale
Aligning AI specialization with departmental structure offers several advantages:
Natural Domain Boundaries: Departments provide clear organizational separations that
correspond to distinct knowledge domains, data types, and operational procedures.
Data Ownership Clarity: Each department understands its data landscape, including structured
databases, documents, and procedural knowledge, facilitating more effective data ingestion and
expert training.
Governance Alignment: Technical system boundaries mirror organizational responsibility
boundaries, simplifying accountability, maintenance, and compliance oversight.
Distributed Expertise: Departments can contribute domain knowledge to their respective expert
training while central IT manages shared infrastructure.
3.3 Architecture Components
3.3.1 Large Generalist Foundation Model
The generalist model serves multiple roles:
•Fallback Handler: Processes queries that span multiple departments or require broad
contextual understanding
•World-Model Generator: Produces structured knowledge representations for
departmental expert training
•Quality Arbiter: Provides verification and synthesis capabilities for multi-departmental
queries
•System Coordinator: Handles complex queries requiring coordination across
departmental boundaries
3.3.2 Departmental Domain Experts
Each departmental expert is designed for specific organizational contexts:
HR Expert: Handles employee policies, benefits, compliance procedures, and organizational
guidelines Finance Expert: Processes budget policies, expense procedures, financial reporting
requirements, and accounting standards IT Expert: Manages technical documentation, system
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4. procedures, security policies, and infrastructure guidelines Legal Expert: Addresses compliance
requirements, regulatory guidance, contract templates, and legal procedures Operations Expert:
Covers process documentation, quality standards, vendor procedures, and operational guidelines
Each expert maintains:
•Capability Profile: Explicit specification of handled query types and performance
expectations
•Data Integration: Connections to relevant departmental data sources and document
repositories
•Update Mechanisms: Procedures for incorporating new departmental knowledge and
policy changes
4. World-Model Distillation for Departmental Experts
4.1 Distillation Process Overview
World-model distillation transfers structured knowledge from the generalist model to
departmental experts through explicit representation extraction and specialized training
protocols.
4.2 World-Model Generation
The generalist model generates department-specific world models containing:
Conceptual Knowledge:
•Domain-specific entities (employee types, budget categories, system components)
•Organizational hierarchies and relationships
•Policy frameworks and compliance structures
Procedural Knowledge:
•Step-by-step processes and workflows
•Decision trees and approval chains
•Exception handling and escalation procedures
Relational Knowledge:
•Dependencies between departments and processes
•Cross-functional workflow connections
•Compliance and regulatory relationships
Contextual Knowledge:
•Organizational culture and communication norms
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5. •Historical precedents and decision patterns
•Risk factors and mitigation strategies
4.3 Structured Knowledge Extraction
4.3.1 Department-Specific Prompting
The generalist model receives structured prompts to extract departmental knowledge:
Generate a comprehensive world model for the [DEPARTMENT] department including:
1. Core concepts and entities relevant to [DEPARTMENT] operations
2. Key processes and procedures used by [DEPARTMENT]
3. Relationships between [DEPARTMENT] and other organizational units
4. Compliance requirements and regulatory constraints
5. Common decision patterns and approval workflows
6. Exception handling and escalation procedures
4.3.2 Knowledge Representation Format
Extracted knowledge is structured using established representation frameworks:
Knowledge Graphs: Entity-relationship structures representing departmental concepts and their
connections
Process Models: Workflow representations capturing procedural knowledge and decision points
Rule Systems: Logical constraints representing policies, regulations, and compliance
requirements
Ontologies: Formal vocabularies defining departmental terminology and concept hierarchies
4.4 Expert Training Protocol
4.4.1 Multi-Objective Training
Departmental experts are trained using multiple loss functions:
Python:
# Classification loss for query routing accuracy
L_task = CrossEntropy(expert_prediction, ground_truth)
# Knowledge alignment loss for concept recognition
L_concepts = MSE(expert_concepts, distilled_concepts)
# Procedural consistency loss for workflow adherence
L_procedures = ConsistencyLoss(expert_procedures, department_procedures)
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6. # Cross-departmental coherence loss
L_coherence = CoherenceLoss(expert_response, related_departments)
# Combined training objective
L_total = α*L_task + β*L_concepts + γ*L_procedures + δ*L_coherence
4.4.2 Training Data Generation
Training datasets combine:
•Synthetic Queries: Generated by the generalist model based on departmental world
models
•Historical Queries: Actual departmental queries from help desk systems, email, and
documentation requests
•Cross-Reference Validation: Queries validated against actual departmental experts and
procedures
5. Confidence-Threshold Router with Expert Self-
Description
5.1 Router Architecture
The routing system employs a single neural classifier trained to estimate query-department
alignment and provide confidence scores for routing decisions.
5.2 Expert Capability Profiles
Each departmental expert provides structured capability descriptions:
JSON
{
"department": "Human Resources",
"expert_id": "hr_specialist",

"query_capabilities": {
"policy_questions": 0.95,
"benefits_inquiries": 0.92,
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7. "compliance_guidance": 0.88,
"organizational_procedures": 0.90,
"employee_relations": 0.85
},

"domain_coverage": [
"Employee handbook policies",
"Benefits administration",
"Performance management",
"Compliance procedures",
"Organizational development"
],

"boundary_conditions": {
"minimum_confidence": 0.75,
"known_limitations": [
"Legal interpretations (defer to Legal)",
"Budget approvals (defer to Finance)",
"Technical implementations (defer to IT)"
],
"cross_department_triggers": [
"compensation and budget questions",
"policy implementation requiring IT systems",
"compliance matters requiring legal review"
]
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8. },

"performance_metrics": {
"average_accuracy": 0.89,
"response_latency_ms": 200,
"user_satisfaction": 0.87
}
}
5.3 Routing Decision Logic
Python
def route_enterprise_query(query, router_model, expert_profiles, generalist):
# Step 1: Classify query and estimate confidence
department_prediction, confidence = router_model.predict(query)

# Step 2: Handle explicit multi-department queries
if detect_multi_department(query):
return handle_multi_department_query(query, expert_profiles, generalist)

# Step 3: Route to generalist for low confidence
if confidence < expert_profiles[department_prediction]["minimum_confidence"]:
return generalist

# Step 4: Check capability profile match
if not matches_department_capabilities(query, expert_profiles[department_prediction]):
return generalist

# Step 5: Route to departmental expert
return get_departmental_expert(department_prediction)
5.4 Multi-Department Query Handling
For queries spanning multiple departments:
5.4.1 Parallel Consultation
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9. Python
def handle_multi_department_query(query, expert_profiles, generalist):
relevant_departments = identify_relevant_departments(query)

if len(relevant_departments) <= 3: # Manageable complexity
responses = []
for dept in relevant_departments:
expert_response = get_departmental_expert(dept).query(query)
responses.append(f"{dept}: {expert_response}")

# Synthesize using generalist
combined_context = "\n".join(responses)
return generalist.synthesize(query, combined_context)
else:
# Too complex, route directly to generalist
return generalist.query_with_department_context(query, relevant_departments)
5.4.2 Conservative Fallback Strategy
When multi-department handling becomes complex:
•Route to generalist with enriched context from relevant departmental data sources
•Avoid attempting complex synthesis that may introduce errors
•Maintain audit trail of routing decisions for analysis and improvement
•
6. Implementation Framework
6.1 Deployment Architecture
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10. 6.1.1 Infrastructure Requirements
Computational Resources:
•Central server cluster for generalist model (high-memory, GPU-accelerated)
•Distributed departmental expert deployment (moderate resources per department)
•Load balancer and routing infrastructure
Data Integration:
•Secure connections to departmental data sources
•Document processing pipelines for each department
•Version control and change management systems
Security and Governance:
•Network isolation between departmental systems
•Access control and authentication mechanisms
•Audit logging and monitoring systems
6.1.2 Departmental Onboarding Process
Phase 1: Assessment and Planning (2-4 weeks)
•Departmental data inventory and classification
•Expert capability requirements definition
•Integration planning with existing systems
Phase 2: World-Model Generation (1-2 weeks)
•Generalist model extraction of departmental knowledge
•Expert review and validation of extracted world models
•Refinement and customization of knowledge representations
Phase 3: Expert Training (2-6 weeks)
•Departmental expert training using distilled world models
•Integration with departmental data sources
•Performance validation and capability profile creation
Phase 4: Router Integration (1-2 weeks)
•Router training data generation including new department
•Confidence threshold calibration and testing
•Integration testing with existing departmental experts
Phase 5: Production Deployment (1-2 weeks)
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11. •Staged rollout to departmental users
•Performance monitoring and feedback collection
•Fine-tuning based on real usage patterns
6.2 Governance and Maintenance
6.2.1 Departmental Responsibilities
Data Management: Each department maintains their expert's data sources and ensures currency
of information
Quality Assurance: Departmental subject matter experts validate their AI expert's responses and
provide feedback
Capability Updates: Departments update their capability profiles as procedures and knowledge
evolve
User Training: Departments train their personnel on effective interaction with their AI expert
6.2.2 Central IT Responsibilities
Infrastructure Management: Central IT maintains the technical infrastructure, monitoring, and
security systems
Router Maintenance: Central team manages router training, confidence calibration, and cross-
departmental coordination
Generalist Model Operations: Central team operates and maintains the large generalist model
and world-model generation capabilities
System Integration: Central team manages integrations between departmental experts and
enterprise systems
7. Evaluation Framework
7.1 Performance Metrics
7.1.1 Routing Accuracy
•Single-Department Queries: Percentage of queries correctly routed to appropriate
departmental expert
•Multi-Department Queries: Effectiveness of parallel consultation and synthesis
•Fallback Appropriateness: Quality of generalist routing decisions for complex or
ambiguous queries
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12. 7.1.2 Response Quality
•Departmental Expert Accuracy: Correctness of responses within each department's
domain
•Consistency: Alignment of expert responses with departmental policies and procedures
•Completeness: Coverage of relevant information in expert responses
7.1.3 Efficiency Measures
•Computational Efficiency: Resource utilization compared to generalist-only systems
•Response Latency: End-to-end response times across different query types
•System Throughput: Concurrent query handling capacity
7.1.4 User Satisfaction
•Relevance: User assessment of response relevance to their queries
•Usability: Ease of interaction and system navigation
•Trust: User confidence in system responses and recommendations
7.2 Evaluation Protocols
7.2.1 Benchmark Development
•Departmental Query Sets: Curated collections of typical queries for each department
•Cross-Department Scenarios: Test cases requiring multi-departmental knowledge
•Edge Cases: Queries designed to test system boundaries and failure modes
7.2.2 Continuous Monitoring
•Real-Time Performance Tracking: Ongoing measurement of routing accuracy and
response quality
•User Feedback Integration: Systematic collection and analysis of user satisfaction data
•A/B Testing: Comparative evaluation of system improvements and configuration changes
8. Challenges and Mitigation Strategies
8.1 Technical Challenges
8.1.1 Router Training Complexity
Challenge: Training the router to understand departmental boundaries and query patterns across
diverse organizational contexts.
Mitigation Strategies:
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13. •Implement staged deployment starting with clearly differentiated departments
•Use conservative confidence thresholds that favor generalist routing for ambiguous cases
•Employ active learning to improve router performance based on user feedback and
correction
8.1.2 World-Model Extraction Reliability
Challenge: Ensuring that extracted world models accurately represent departmental knowledge
and procedures.
Mitigation Strategies:
•Implement validation protocols involving departmental subject matter experts
•Use multiple extraction methods and cross-validation techniques
•Maintain version control and rollback capabilities for world models
8.1.3 Multi-Department Query Synthesis
Challenge: Combining responses from multiple departmental experts into coherent, accurate
answers.
Mitigation Strategies:
•Limit parallel consultation to manageable numbers of departments (≤3)
•Implement conservative fallback to generalist for complex multi-department queries
•Develop specialized synthesis prompts and validation procedures
8.2 Organizational Challenges
8.2.1 Departmental Cooperation
Challenge: Ensuring consistent participation and data quality across departments with varying
technical capabilities and priorities.
Mitigation Strategies:
•Establish clear governance structures and responsibilities
•Provide standardized onboarding tools and support
•Implement performance incentives and recognition programs
8.2.2 Change Management
Challenge: Managing updates and maintenance across multiple departmental systems and
experts.
Mitigation Strategies:
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14. •Develop automated update detection and notification systems
•Implement staged deployment procedures for system changes
•Create comprehensive documentation and training programs
8.2.3 Quality Consistency
Challenge: Maintaining consistent quality across departmental experts with different levels of
data quality and expert involvement.
Mitigation Strategies:
•Establish minimum quality standards and certification processes
•Implement cross-departmental benchmarking and best practice sharing
•Provide ongoing technical support and consultation
9. Case Study: Implementation Scenario
9.1 Scenario Overview
Consider a mid-sized enterprise (5,000 employees) with six major departments implementing the
departmental MoE architecture:
Participating Departments:
•Human Resources (HR)
•Information Technology (IT)
•Finance
•Legal and Compliance
•Operations
•Sales and Marketing
9.2 Implementation Timeline
Month 1-2: Infrastructure setup and generalist model deployment Month 3-4: HR and IT expert
development (high data quality, clear boundaries) Month 5-6: Finance and Legal expert
development Month 7-8: Operations and Sales expert development Month 9-10: Multi-
department integration and router optimization Month 11-12: Full deployment and performance
optimization
9.3 Expected Outcomes
Computational Efficiency: 60-70% reduction in generalist model usage through effective
departmental routing
Response Quality: 15-25% improvement in response relevance for department-specific queries
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15. User Satisfaction: Improved user experience through faster, more targeted responses
Organizational Benefits: Better knowledge accessibility and consistency across departments
9.4 Risk Factors
Technical Risks: Router miscalibration leading to poor routing decisions, world-model
extraction failures
Organizational Risks: Departmental resistance to participation, inconsistent data quality,
inadequate maintenance commitment
Mitigation Approach: Staged deployment, comprehensive monitoring, strong governance
framework
10. Future Directions
10.1 Advanced Routing Techniques
Hierarchical Department Routing: Sub-departmental specialization for large organizations
with complex departmental structures
Dynamic Expert Selection: Adaptive routing based on real-time expert performance and
availability
Cross-Enterprise Integration: Federation approaches for multi-organization deployments
10.2 Enhanced World-Model Distillation
Automated Knowledge Extraction: Improved methods for extracting structured knowledge
from unstructured departmental data
Incremental Learning: Approaches for continuously updating departmental experts without full
retraining
Cross-Department Knowledge Transfer: Methods for sharing relevant knowledge between
related departments
10.3 Advanced Evaluation Methods
Simulation-Based Testing: Comprehensive testing using simulated organizational scenarios
Longitudinal Studies: Long-term evaluation of system performance and organizational impact
Comparative Analysis: Evaluation against alternative enterprise AI deployment approaches
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16. 11. Conclusion
The departmentally-aligned mixture-of-experts architecture presents a promising approach for
enterprise AI deployment that addresses key organizational requirements while maintaining
computational efficiency and governance clarity. By leveraging world-model distillation and
confidence-based routing, this approach enables organizations to deploy sophisticated AI systems
that align with their existing structure and expertise.
Key advantages include natural alignment with organizational boundaries, distributed
maintenance responsibilities, and clear governance pathways. However, implementation requires
careful attention to router training, world-model extraction reliability, and organizational change
management.
Success depends on staged deployment, conservative system design, and strong organizational
commitment to maintenance and quality assurance. Organizations considering this approach
should begin with well-defined departments that have clear data and expertise, then expand based
on empirical evidence of effectiveness.
The architecture represents a practical evolution of mixture-of-experts systems toward real-world
organizational deployment, providing a foundation for more sophisticated enterprise AI systems
while maintaining the simplicity and reliability essential for production use.
References
[1] Hinton, G., Vinyals, O., and Dean, J. Distilling the Knowledge in a Neural Network. NIPS
Deep Learning and Representation Learning Workshop, 2015.
[2] Shazeer, N., et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-
Experts Layer. International Conference on Learning Representations, 2017.
[3] Booth, H., et al. Developing the NCCoE Chatbot: Technical and Security Learnings from the
Initial Implementation. NIST Internal Report 8579, 2025.
[4] Gururangan, S., et al. DEMix Layers: Disentangling Domains for Modular Language
Modeling. Proceedings of the 59th Annual Meeting of the Association for Computational
Linguistics, 2021.
[5] Kim, B., et al. Interpretability Beyond Feature Attribution: Testing with Concept Activation
Vectors. International Conference on Machine Learning, 2018.
[6] Schölkopf, B., et al. Toward Causal Representation Learning. Proceedings of the IEEE,
109(5):612-634, 2021.
[7] Lewis, P., et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
Conference on Neural Information Processing Systems, 2020.
Page 16

17. [8] Lepikhin, D., et al. GShard: Scaling Giant Models with Conditional Computation and
Automatic Sharding. International Conference on Learning Representations, 2021.
[9] Romero, A., et al. FitNets: Hints for Thin Deep Nets. International Conference on Learning
Representations, 2015.
[10] Mao, J., et al. The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and
Sentences From Natural Supervision. International Conference on Learning Representations,
2019.
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