TimeLike

Steve Coy founded TimeLike Systems in 2011.² The framework descends from tempus, the general-purpose simulation framework Coy led at MZA Associates, whose optics tool WaveTrain became a standard in high-energy laser research.²

The company calls its approach SECHI, simulation-enabled cooperative human intelligence.¹ Experts encode what they know as causal models, models of what causes what, and these are coupled into a model of a larger system.¹ The simulation predicts the probable consequences of each course of action open to the user.¹ Model-based optimisation then narrows them to the course with the best expected outcome.¹

The engine is event-driven. Models are blocks with typed ports.³ A block does nothing until something it is connected to changes, asks it for a value, or a scheduled event fires.³ So each block can carry its own physics and run at its own pace.³ Queries carry time, and causality falls out of the coupling.³ Components negotiate their fidelity with their neighbours instead of an expert tuning every run.³ The aim is a virtual clock that runs faster than the real one: a model fast enough to predict can act as a controller for a place.³

TimeLike contains no machine learning of its own.^1,3 The company presents it as intelligence augmentation and distinguishes it from generative AI.¹ A patent application, "Composable Self-Modeling Cyber-Physical Systems", is pending.^1,3 Earlier tools in the lineage ran satellite-operator training in real time and wrapped a legacy Fortran adaptive-optics code.³

TimeLike is not yet released.¹ The company is looking for collaborators, strategic partners and early adopters.^1,4 There is no public download, and pricing is not published.¹