I. Capability without portability is fragile

Modern AI models are fundamentally mismatched with the environments in which they must operate. Best-in-class architectures assume centralized resources, abundant bandwidth, and unlimited energy. Optimization strategies collapse under physical constraints.

The result: powerful models that cannot function where they are most needed.

II. Constraints must become first principles

Most efforts treat real-world constraints as technical obstacles. We believe they must be treated as primary design parameters. Latency, power, memory, and locality are not externalities. They are the environment in which intelligence must survive.

III. Restructuring models for native execution

Wisp is building a new runtime layer that reshapes complex models into forms that are natively efficient, adaptable, and deployable.

Real-world constraints are not patched on after deployment. They are internalized as core properties of execution itself—without retraining, without rewriting, and without sacrificing capability.

Edge inference makes trained models smaller so they can fit onto devices. Wisp is restructuring models so that operating within real-world constraints becomes part of their core behavior, not a compromise layered on after.

Models restructured this way are more stable under resource constraints, more adaptable across fragmented environments, and capable of real-time performance without centralized fallback.

This is the difference between surviving real-world constraints, and being engineered for them.