We engineer native runtimes, memory-tiered architectures, and sovereign AI tooling. Built on Rust, C++, and Linux kernel primitives — maximizing hardware leverage while keeping critical data strictly on-device.
Enterprise-grade document redaction, PII sanitization, and LLM token optimization running 100% client-side.
Most data leaks don't come from malicious breaches — they occur when sensitive intellectual property, API credentials, and personal identifiable information (PII) are accidentally pasted into public LLM interfaces. DocSanitizer eliminates this risk through high-speed, local-only token sanitization and context compression before prompts ever touch an API.
Pushing computational efficiency at the boundary of hardware, kernel interfaces, and neural model architectures. We believe real breakthroughs come from deep systems optimization, not just adding more cloud compute.
High-throughput asynchronous tensor tiering engine. Enables running and fine-tuning large machine learning models beyond physical GPU VRAM capacity by offloading and prefetching weights across Host DRAM and NVMe storage via direct IO rings.
Novel audio synthesis paradigm coupling digital signal processing (DSP) multirate filter banks with decoupled hierarchical transformers. Separates high-level semantic intent from low-level acoustic reconstruction to achieve ultra-low bitrate neural codec fidelity.
Zero-overhead in-kernel telemetry harness using modern BPF CO-RE (Compile Once – Run Everywhere). Dynamically profiles scheduler latency, page fault rates, and memory contention during intensive AI model execution to auto-tune kernel sysctl parameters.
Firmware customizations and kernel drivers for deterministic hardware control. Includes cycle-accurate software PWM drivers, hardened OpenWrt router distributions, and local mesh communication frameworks that operate independently of centralized cloud dependencies.
Software has become bloated, extractive, and recklessly reliant on centralized servers. We build by three principles.
Your computation and sensitive inputs must never become training fodder or be stored on arbitrary cloud disks. When software claims to be private, that guarantee must be proven by architecture, not vague terms of service.
Before renting more GPU clusters or spinning up serverless functions, optimize the memory layout, vectorize the loops, and streamline the data path. Deep systems engineering yields 10x gains in speed and efficiency without additional hardware costs.
No hidden tracking scripts, no third-party behavioral trackers, and no opaque telemetry. Code should be deterministic, runtimes should be transparent, and user trust should be earned through verifiable behavior.