Runtime Logic Designed for Control and Clarity
Optilyx gives full control over how optimization runs are executed. You define when to stop, what to prioritize, and how performance is measured , whether for fast decisions or deeper exploration.
Time-Bound Execution
Run for a fixed time window to support rapid re-optimization and time-sensitive planning.
Quality-Based Execution
Run until target objectives are reached, such as yield, cost threshold, or performance score.
Cost-Limited Execution
Set a compute or financial budget. Optilyx will optimize within defined usage or consumption limits.
1
Configuration Manager
Defines the structure of the optimization problem including objectives, constraints, and parameters. It prepares the environment for execution.
2
Evaluation Manager
Connects to your models to test candidate solutions. It supports black box evaluations and model-based feedback.
3
Strategies Manager
Explores the solution space using advanced search logic. It generates high quality alternatives based on defined goals and feedback.
4
Memories Manager
Stores past evaluations and decisions to avoid redundant work. It guides future searches using learned patterns.
5
Intelligence Manager
Learns from performance over time. It identifies promising areas in the search space and adapts strategy selection dynamically.
Optimization Intelligence That Outperforms
Engineered for speed, scale, and precision, across any industry, any model, any constraint.
Powering Real-Time Decisions
Optilyx uses metaheuristics and adaptive learning. It is built to embed within existing systems, helping engineers and data scientists solve large combinatorial problems.
Core Modular Architecture
Optilyx is a modular optimization solver, with each component performing a distinct function. Modules can be customized, extended, and evolved over time.
Optilyx powers applications as a back end service. It connects through API input and output to models, systems, and workflows. It runs behind the scenes as a decision layer in your architecture.
Optilyx Key Features
1
Multiple Objectives
Handles competing objectives by dynamically balancing priorities within the solution space to deliver optimized decisions that reflect trade-offs in real-world constraints.
2
Advanced Problem Structure
Solves complex optimization problems with linear, non-linear, and mixed-integer variables, without simplifying constraints or reducing objectives to a single weight.
3
Cloud Native Sclaing
Adapts compute usage to match problem size, complexity, and urgency. Enables GPU acceleration and distributed parallel solving.
4
Explainability & Sensitivity Analysis
Trace every outcome back to its source. Understand how key variables affect results, and explore what-if scenarios with built-in sensitivity feedback.
5
API-First and Black-Box Compatible
Integrates via API, sending inputs and receiving evaluated outputs. Models remain black box with no exposure of internal structure or raw data.
6
Real-Time Re-Optimization
Re-solves instantly as new data, constraints, or disruptions occur. Maintains optimization progress to accelerate convergence under dynamic conditions.