Building intelligence that adapts to you
Most AI systems analyze moments. We analyze decades. Here's what we discovered when we started processing 10-year data windows: patterns that repeat annually become visible, cognitive rhythms emerge across seasons, and true behavioral baselines reveal themselves.
The difference between 1 year and 10 years of data isn't linear—it's exponential. Long-term patterns only become visible with sufficient temporal depth.
Traditional AI responds the same at 6am and 6pm. But your brain doesn't work that way. We built Sovria to recognize and adapt to cognitive states—hyperfocus, recovery, creative flow, analytical peaks.
The technical challenge: detecting these states in real-time from behavioral patterns without invasive monitoring. Solution: cross-correlation of calendar patterns, response timing, and task complexity preferences.
Why two nodes instead of one powerful machine? F1 (Mac Studio) handles pattern recognition with unified memory architecture. F2 (RTX 4090) runs Llama 70B for language understanding. Separation allows air-gapping, specialized optimization, and failover redundancy.
This architecture mirrors how your brain processes—pattern recognition separate from language generation, working in parallel.
Cloud AI is fundamentally incompatible with true privacy. Every query trains their model. Every pattern becomes their property. We chose the harder path: everything local, nothing shared.
The $50K price point isn't arbitrary—it's what true data sovereignty costs when you refuse to subsidize hardware with data extraction.
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