System Architecture

Sematryx is built on a modular, service-oriented architecture designed for high-performance optimization, explainability, and continuous learning. The system uses a multi-agent "Council of Experts" approach to intelligently select and execute optimization strategies.

The Sematryx architecture is protected by multiple pending patents covering adaptive optimization, temporal intelligence, knowledge systems, and explainability frameworks.

How Sematryx Works

The Optimization Flow

  1. 1.Problem Analysis: The system analyzes your optimization problem to understand its characteristics, constraints, and complexity.
  2. 2.Strategy Selection: Multi-agent intelligence evaluates multiple optimization strategies and selects the best approach through consensus-based decision making.
  3. 3.Tournament Validation: Candidate strategies compete in short, low-budget rounds to empirically prove performance before full execution.
  4. 4.Optimization Execution: The selected strategy runs with full computational resources to find the optimal solution.
  5. 5.Learning & Storage: Results are analyzed, explained, and stored in the knowledge system to improve future optimizations.

Core Components

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Platform Services

The gateway layer that handles request validation, authentication, billing, and transforms optimization results into human-readable explanations. This layer ensures security, scalability, and provides the API interface for all client interactions.

  • • API Gateway & Routing
  • • Authentication & Authorization
  • • Explainability Engine
  • • System Diagnostics & Monitoring
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Intelligence Layer

The "Council of Experts" - a multi-agent system where specialized AI agents collaborate to analyze problems, evaluate strategies, and reach consensus on the best optimization approach. This layer provides the intelligence that makes Sematryx adaptive and self-improving.

  • • Multi-Agent Coordinator
  • • Research & Validation Agents
  • • Consensus Engine
  • • Meta-Policy Learning
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Optimization Engine

The mathematical core that executes optimization strategies. The engine maintains a library of multiple optimization algorithms (evolutionary, Bayesian, gradient-based, and specialized methods) and uses a tournament system to validate strategy performance before full execution.

  • • Multi-Strategy Library
  • • Tournament System
  • • Asynchronous Execution
  • • Adaptive Strategy Selection
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Knowledge & Memory

Long-term persistence systems that enable the platform to learn from every optimization. This "Adaptive Intelligence" allows Sematryx to recognize similar problems, recall successful strategies, and improve performance over time through pattern recognition and relationship mapping.

  • • Vector Memory (Problem Embeddings)
  • • Knowledge Graph (Relationships)
  • • Temporal Context Tracking
  • • Causal Discovery & Analysis

Optimization Strategy Library

Sematryx maintains a comprehensive library of optimization algorithms, each suited for different problem types and characteristics. The system intelligently selects from these strategies based on problem analysis and historical performance.

Evolutionary Strategies

  • • Differential Evolution
  • • CMA-ES (Covariance Matrix Adaptation)
  • • Genetic Algorithms

Bayesian Methods

  • • Gaussian Process Optimization
  • • Random Forest Bayesian
  • • Tree-structured Parzen Estimators

Global Optimization

  • • Dual Annealing
  • • Simplicial Homology (SHGO)
  • • Quantum-Inspired Methods

Specialized Methods

  • • Mixed-Integer Optimization
  • • Constraint-Handling Algorithms
  • • Domain-Specific Optimizers

Architectural Principles

Modular & Service-Oriented

Each component operates independently with well-defined interfaces, enabling scalability, maintainability, and the ability to upgrade individual systems without affecting others.

Intelligence-Driven Selection

Rather than using a single algorithm, Sematryx uses multi-agent intelligence to analyze problems and select the optimal strategy, often combining multiple approaches for best results.

Continuous Learning

Every optimization contributes to the system's knowledge base, enabling recognition of similar problems and faster, better solutions over time through pattern recognition and strategy refinement.

Explainability by Design

Explainability is built into the architecture, not added as an afterthought. The system provides natural language explanations, visual diagnostics, and decision lineage for regulatory compliance and stakeholder understanding.

Performance Characteristics

Cold Start (New Problems)

For novel or complex problems, the system uses the full multi-agent intelligence pipeline:

  • • Problem analysis & characterization
  • • Multi-agent strategy evaluation
  • • Tournament validation
  • • Full optimization execution

Hot Path (Recognized Problems)

For problems similar to those seen before, the system can bypass agent consensus:

  • • Vector memory recognition
  • • Direct strategy recall
  • • Immediate execution
  • • Sub-200ms latency