RevoBolic

Software Development Powerhouse

Projects & Services

Enigmus — Private AI, Locally

Enigmus

Private on-device AI for macOS, iOS, and iPadOS. Built on MLX. Accelerated with Metal. Supports open-weight transformer models including Qwen and GPT-OSS. Quantized 4-bit and 8-bit weights reduce unified-memory footprint and enable larger models on constrained hardware. Tokenization, KV-cache management, sampling, and streaming generation run natively in Swift. No cloud inference. No remote state. No external dependency in the execution path.

Apple Silicon chip architecture — GPU compute cores and unified memory

Native Apple Development

Implemented directly against the Apple stack: Swift, MLX, Metal, Accelerate, and system-level memory primitives. Designed for real Apple Silicon constraints, including bandwidth ceilings, thermal limits, and unified-memory pressure. Uses memory-mapped model loading, async scheduling, zero-copy tensor paths where possible, and GPU dispatch behavior tuned for chip-specific performance characteristics.

Privacy-first architecture with encrypted vault and shield

Privacy-First Architecture

Local execution is the default trust model. Prompts, model state, caches, and persistent data remain inside the app sandbox. Inference runs in an isolated local process. Data stays within the app container and is protected by OS-level isolation and encryption at rest. The system assumes zero trust outside the device boundary.

Engineering consulting — system architecture and design

Consulting

Engineering support for teams building local AI systems on Apple platforms. Scope includes quantization, MLX integration, Metal profiling, memory-budget analysis, inference runtime design, and architecture review for private on-device deployment.