SUAV.AI is a private assistant on your phone. It reads your own notes and files and answers with citations — on-device, and it works offline. No cloud, no account, no data collection. When you're online it can reach a curated knowledge service for cited answers — only if you turn it on.
Smaller, faster inference is table stakes — free tools already get ~30% losslessly. The hard part, the part your risk team can't sign off on, is proving the smaller model still behaves. We hand you that proof: a receipt you can check — bit-exact on certified builds.
Every figure ties to a logged, reproducible experiment. The full result set lives in the vault.
Every other tool ships a promise — “almost as good, trust us.” We ship a receipt: what you deploy reproduces the reference we certified, proven by direct test on real hardware — bit-for-bit on certified builds, within a measured tolerance across other kernels. And it follows the model to the edge: the certified build generates token-for-token identically on a real edge device (a Jetson), not just the datacenter GPU it was built on. It is the difference between “we pruned it and it seems fine” and “here is the identity proof” — for your risk team, your auditor, your customers. (Not to be confused with commodity lossless repacking — same model, ~30% fewer bytes, no faster; useful, and increasingly free.)
Every claim on this site was scored against a prediction we froze before we ran the experiment — and we publish the misses next to the hits. That is the whole difference between a benchmark and a receipt.
That is not marketing discipline — it is the product. A certificate is only worth as much as the lab behind it.
Audits survey the whole method space — our ordering, the standard pruning baselines, and quantization stacking — at matched budgets. You see every option priced, not one vendor's favorite.
Built and run by one founder on a bootstrap budget — billion-parameter experiments on rented A100s, local models on Apple silicon, a filed patent (2026), and every result on the record. So when we say 1.37× and bit-exact, those words mean exactly what they say.