We engineer self-reinforcing loops between Agents and LLMs. A symbiotic architecture where systems teach each other—maximizing capability while minimizing cost.
Our core architecture allows Agents to curate high-value data for LLMs, while LLMs refine Agent logic in real-time. A continuous cycle of improvement.
Smarter agents require fewer tokens. Our mutual learning loops distill context to its essence, drastically reducing LLM inference costs and the total operational expense of the system.
We build systems that don't just execute commands, but evolve their capabilities. The next generation of self-correcting software.