Decision Intelligence is the process of computing the best possible action by exploring a large search space of decision alternatives. It relies on optimization to evaluate complex trade-offs, constraints, and objectives. Optilyx can navigate vast search spaces to identify high-quality solutions that align with real-world goals and operational limits.

Optilyx is built for large, complex decision problems that involve constraints, multiple objectives, and evolving inputs. Unlike traditional solvers and mathematical tools, it does not depend on linear approximations, or weighted objectives. Instead, it uses advanced metaheuristics, adaptive learning, and memory-guided search to explore large solution spaces. 

Yes. Optilyx is cloud-native, parallel-ready, and dynamically allocates compute resources based on problem size and complexity. It can handle large, high-dimensional search spaces that overwhelm traditional solvers.

Optilyx interacts with models through API input and output, without needing access to internal logic. It treats your model as a black box, sending inputs, receiving evaluated outputs, and optimizing based on those results. This allows Optilyx to work with simulations, custom logic, or proprietary systems without requiring model reformulation or exposure of sensitive IP.

Optilyx connects to your existing infrastructure through secure APIs. It does not require data migration or model rebuilds. You can use your current models, tools, and planning logic and Optilyx interacts with them via input and output calls. 

Optilyx does not access, store, or transfer your raw data. It interacts with your models through input and output only, evaluating results without requiring visibility into underlying data. It operates in secure environments with read-only access if needed, and supports SOC2-ready deployments for enterprise compliance.

Optilyx uses a consumption-based pricing model, so you only pay for the optimization compute you use. There are no per-user fees, platform charges, or costs for idle infrastructure. Runtime can be controlled by compute time, solution quality, or cost thresholds. Optilyx supports flexible deployment without long-term licensing commitments or lock-in.

Yes. Every result from Optilyx is traceable to the inputs, constraints, and decisions that shaped it. Built-in sensitivity analysis helps identify which variables drive outcomes and allows for what-if exploration before implementation.

Optilyx is designed to support real-time or near-real-time decision loops. It maintains memory between solves and adapts quickly to input changes, making it suitable for scheduling, logistics, and other time-sensitive applications.

Optilyx is a form of AI, but it is not based on machine learning or large language models. It does not generate content or predict outcomes from training data. Instead, it uses advanced optimization to compute the best possible decision in complex environments. Optilyx explores the space of possible actions using adaptive strategies and evaluates what to do next based on goals, constraints, and system feedback.