Turn Models into Purposeful, Adaptive Enablers.
Back to HomeMaking models 'come alive'—continuously learning and collaborating adaptively as software evolves.
ModelCopilot is a platform that transforms models—spanning prior knowledge systems, machine learning frameworks, and more—into dynamic, context-aware collaborators. It ensures models remain relevant, actionable, and continuously evolving throughout the lifecycle of complex intelligent software systems, from conceptualization and design to adaptation and autonomous evolution.
We have implemented 239 out of 250 concrete syntax elements and 452 out of 514 concrete syntax elements to enable the construction of models using KerML and SysML V2 textual notations.
Building software that redefines what’s possible with ModelCopilot
Support end-to-end model construction, evolution, and orchestration—enabling traditional, AI-driven, and hybrid approaches to work together as a dynamic ensemble.
Seamlessly integrate models into workflows for real-time monitoring, anomaly detection, and adaptive decision-making.
Comprehensively evaluate models and integrated functionalities, ensuring that every aspect of the system—from performance to uncertainty—is rigorously appraised for robust and reliable outcomes.
Models don’t just provide static insights—they grow, adapt, and respond to changing requirements, ensuring long-term value in evolving software ecosystems.
From design to continuous refinement, ModelCopilot embeds models into every phase, keeping them actively engaged in decision-making and system evolution.
Unify diverse modeling approaches—domain expertise, data-driven AI, and more—to tackle complex challenges with precision and adaptability.
Tackle internal and external uncertainties—including the inherent, shallow, and deep ambiguities of AI—by transforming unknown unknowns into known unknowns and converting known unknowns into actionable insights.
