At AUSIAQ AI, our foundational focus in the MVP phase is engineered around a singular, highly ambitious goal: executing complex agentic capabilities directly within local and edge environments. To achieve this while prioritizing absolute privacy and security, we are currently developing a specialized sub-300M parameter model.
Optimizing for Agentic Execution
Unlike massive generalized reasoning models, our sub-300M model is specifically optimized for agentic task execution. By stripping away generalized bloat and focusing entirely on deterministic agentic capabilities, we are achieving a 70% to 80% reduction in token usage and operational costs.
Drastic Latency Reduction
To make edge agentic systems viable for enterprise, speed is critical. We are utilizing parallel execution architectures alongside advanced prompt and memory caching to drastically cut latency. This allows our small language model (SLM) to orchestrate complex, multi-step workflows in a fraction of the time it takes a traditional LLM to ping a cloud server.
The Future: Physical AI Integration
This edge architecture is the stepping stone for our future projections: Physical AI Integration. By running highly optimized agentic models locally, we will seamlessly translate complex tasks into physical actions, enabling direct, low-latency control over real-world devices and robotics.