Domain Libraries

Industry-specific optimization libraries with built-in compliance, constraints, and best practices.

Production-ready solutions for finance, healthcare, supply chain, and more. No need to build from scratch—leverage domain expertise built into every library.

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Available Domain Libraries

Financial Services

Portfolio optimization, risk management, trading strategies, and regulatory compliance.

Use Cases:

  • •Portfolio allocation with Basel III, MiFID II compliance
  • •Risk-adjusted return optimization
  • •Trading strategy backtesting
  • •Credit risk modeling

Key Features:

  • Regulatory compliance built-in
  • CVaR, VaR, and advanced risk metrics
  • Explainable decisions for audit trails
  • Multi-objective optimization (return vs risk)
Financial Services Example
from sematryx.domains import finance

result = finance.optimize_portfolio(
    returns=asset_returns,
    covariance=cov_matrix,
    constraints={
        'max_position': 0.30,
        'min_return': 0.08,
        'regulatory': 'basel_iii'
    },
    risk_measure='cvar',
    explanation_level=4
)

Healthcare

Clinical optimization, resource allocation, treatment protocols, and patient safety.

Use Cases:

  • •Hospital staff scheduling with union rules
  • •Clinical trial design optimization
  • •Drug discovery pipeline optimization
  • •Resource allocation with safety constraints

Key Features:

  • HIPAA-compliant data handling
  • Patient safety constraints (hard constraints)
  • Skill coverage and shift balancing
  • Emergency capacity buffers
Healthcare Example
from sematryx.domains import healthcare

result = healthcare.optimize_scheduling(
    resources={'nurses': 50, 'doctors': 20},
    demand_forecast=weekly_demand,
    constraints={
        'min_nurse_patient_ratio': 0.25,
        'max_consecutive_shifts': 2,
        'safety_constraints': 'always'
    }
)

Supply Chain

Logistics optimization, inventory management, vehicle routing, and warehouse operations.

Use Cases:

  • •Vehicle routing with time windows
  • •Multi-echelon inventory optimization
  • •Cold chain logistics
  • •Warehouse layout optimization

Key Features:

  • VRP, CVRP, VRPTW variants
  • Perishable goods management
  • Temperature-controlled routing
  • Resilience planning
Supply Chain Example
from sematryx.domains import supply_chain

result = supply_chain.optimize_routing(
    locations=depot_and_customers,
    demands=customer_demands,
    vehicle_capacity=50,
    time_windows=time_constraints
)

Marketing

Campaign optimization, attribution modeling, customer segmentation, and pricing strategies.

Use Cases:

  • •Multi-channel campaign allocation
  • •Attribution modeling
  • •Customer lifetime value optimization
  • •Dynamic pricing strategies

Key Features:

  • Multi-touch attribution
  • Budget allocation across channels
  • ROI optimization
  • A/B testing integration
Marketing Example
from sematryx.domains import marketing

result = marketing.optimize_campaign(
    channels=['search', 'social', 'display'],
    budget=100000,
    objectives=['conversions', 'roas'],
    constraints={'min_brand_awareness': 0.3}
)

AI/ML

Hyperparameter tuning, neural architecture search, and model optimization.

Use Cases:

  • •Deep learning hyperparameter tuning
  • •Neural architecture search
  • •Learning rate scheduling
  • •Reinforcement learning optimization

Key Features:

  • Multi-objective optimization (accuracy vs speed)
  • Early stopping integration
  • Resource-aware optimization
  • Transfer learning support
AI/ML Example
from sematryx.domains import ai_ml

result = ai_ml.optimize_hyperparameters(
    model_type='transformer',
    search_space=hyperparameter_space,
    objectives=['accuracy', 'inference_time'],
    budget=1000  # Max training runs
)

Media

Video encoding, image optimization, rendering, and perceptual quality optimization.

Use Cases:

  • •Per-title video encoding
  • •Image compression optimization
  • •Rendering pipeline optimization
  • •Perceptual quality tuning

Key Features:

  • Quality vs file size optimization
  • Perceptual metrics (SSIM, VMAF)
  • Multi-resolution encoding
  • Adaptive bitrate optimization
Media Example
from sematryx.domains import media

result = media.optimize_video_encoding(
    source_video=video_file,
    target_qualities=['1080p', '720p', '480p'],
    objectives=['quality', 'file_size'],
    perceptual_metric='vmaf'
)

Research

Advanced mathematical optimization, novel algorithms, and quantum optimization.

Use Cases:

  • •Novel algorithm development
  • •Quantum optimization problems
  • •Mathematical research problems
  • •Experimental optimization

Key Features:

  • Cutting-edge algorithms
  • Quantum-inspired optimization
  • Multi-objective research problems
  • Publication-ready results
Research Example
from sematryx.domains import research

result = research.optimize_advanced(
    problem=novel_problem,
    algorithm='quantum_inspired',
    objectives=['optimality', 'novelty'],
    research_mode=True
)

Agents

Multi-agent coordination, budget management, and agent optimization.

Use Cases:

  • •Multi-agent system coordination
  • •LLM selection optimization
  • •Agent budget allocation
  • •Performance monitoring

Key Features:

  • Agent coordination strategies
  • Cost optimization
  • Performance monitoring
  • Dynamic agent selection
Agents Example
from sematryx.domains import agents

result = agents.optimize_coordination(
    agents=agent_pool,
    tasks=task_list,
    budget=1000,
    objectives=['completion_rate', 'cost']
)

Integration

System integration, bridge optimization, and custom domain creation.

Use Cases:

  • •Legacy system integration
  • •Custom domain creation
  • •Pattern library generation
  • •Code generation

Key Features:

  • Custom domain templates
  • Auto code generation
  • Pattern recognition
  • System analysis
Integration Example
from sematryx.domains import integration

# Create custom domain
@integration.register_domain('my_industry')
def my_industry_optimizer(problem):
    # Custom optimization logic
    return optimized_solution

result = integration.optimize(
    domain='my_industry',
    problem=custom_problem
)

Conversational

Create optimization problems through natural language conversation with an AI agent. Perfect for users who want to optimize but aren't familiar with technical concepts.

Use Cases:

  • •Natural language problem description
  • •Guided parameter collection
  • •Automatic domain detection
  • •Dynamic domain extension building

Key Features:

  • LLM-powered problem understanding
  • Interactive parameter collection
  • Real-time input validation
  • Automatic domain matching
Conversational Example
from sematryx.client.sdk import SematryxClient

client = SematryxClient(api_key="YOUR_API_KEY")

# Start conversation with natural language
result = client.start_conversational_optimization(
    description="I want to optimize my marketing budget for maximum ROI"
)

# Interact with agent to collect parameters
# Then complete and optimize
result = client.complete_conversational_optimization(conversation_id)

Why Domain Libraries?

Built-in Compliance

Regulatory compliance (Basel III, HIPAA, etc.) built into domain libraries, not bolted on.

Production-Ready

Pre-configured constraints, validation, and best practices for each industry.

Industry Best Practices

Domain-specific patterns, constraints, and optimization strategies proven in production.

Full Explainability

Domain-aware explanations that speak your industry's language for compliance and trust.

Ready to Get Started?

Start using domain libraries today. All libraries are production-ready and include built-in compliance, validation, and best practices.

Read the DocsDomain Tutorials