ARIA. The right model for the task — not the default one.
Most AI tools route every prompt through a single large model. ARIA evaluates the work in front of it and chooses the right local model for that specific job. Smaller, faster, cheaper, and more accurate where it matters.
Default routing is the most expensive way to be wrong.
A 70-billion-parameter model is overkill for a calendar lookup. A 7-billion-parameter model is wrong for a clinical summary. Most AI deployments run everything through whatever model the vendor defaults to, then absorb the cost and latency that follows.
ARIA inspects the task — its complexity, the data domain, the accuracy bar required — and routes it to the model that fits. A short retrieval question runs on a small model in milliseconds. A multi-step compliance analysis goes to the heavier model. The unit handles both without round-trips.
Not magic. Auditable rules and observability.
Operationally, what does this look like?
A medical-records team using a single cloud model spends a few seconds and a few cents per query. With ARIA on-device, the same team gets sub-second responses on most queries because the work routed correctly to a small local model. The few queries that genuinely need a larger model still get one — they just stay on your hardware.
Your IT team gets a usage dashboard that shows which models ran which workloads and how often. Your compliance officer gets a log they can audit. Your operations lead gets a knob to turn when policy needs to change.
Walk the dashboard before you commit.
Production demo at klamathlounge.com — request the password and we'll send it.