Hyprstream: Secure Versioned AI Models

Regulated industries face a fundamental tension: AI systems need to adapt and improve, yet compliance requires reproducibility, auditability, and strict execution controls.
Hyprstream addresses this challenge by treating models as versioned, Git-native artifacts. Base models and fine-tuned adapters are stored as repositories, enabling branching, commit history, and reproducible rollbacks. Every model state becomes traceable. Every adaptation becomes auditable.
Within a unified runtime, Hyprstream supports both inference serving and LoRA-based fine-tuning, allowing systems to learn from domain-specific data while remaining inside controlled infrastructure. Model branches can be deployed as distinct OpenAI-compatible endpoints, enabling parallel validation, staged rollouts, and controlled promotion to production.
Security is layered by design:
Transport encryption and signed RPC envelopes

Policy-based authorization

Isolated execution via microVM-backed workers

Local-first deployment without mandatory SaaS dependencies

The result is an architecture that supports continuously learning applications while meeting the demands of compliance, traceability, and operational control.
This talk explores how AI systems can evolve without sacrificing governance — and how regulated environments can adopt adaptive AI without relinquishing oversight.