AI that never leaves your building.
Trained on your work. Tuned to your tasks.
Healthcare, legal, finance, and defense can't send their data to a hosted API. The answer is a model that runs on your own hardware, fine-tuned on your own domain, that never phones home. Potara forges it; Auryn is the model; the QPU certificate is the proof it's honest.
Straight talk: an earlier scorecard showed 0.36 → 1.00, but that eval was drawn from the same facts the model was trained on — it measured memorization, not skill. On a genuinely held-out 26-item EDI test (leakage-checked), the fused model scores ~12% vs the base's ~15% and both trail a 7B model (~27%): the fusion does not out-know the base on your domain. What it does deliver is real and verifiable: it runs on your hardware (privacy), is ~3× smaller and ~24% faster, doesn't lose general ability, and reliably reproduces the specific facts and behaviors you explicitly train it on. So we train skills and retrieve facts — a retrieval harness ships with every model, and every "better at your job" claim is proven on a held-out set, per customer. Full benchmark: openauryn/honest_benchmark.json.
Four steps. Your data stays put.
Interview
A chat builds your calibration set: what you do, your tasks, examples. It splits skills (train) from facts (retrieve).
Fuse on your hardware
Merge + calibration-prune + light QLoRA on your own GPU (data never leaves), or our zero-idle cloud. Facts stay fresh via a retrieval harness.
Certify the test
Optionally draw the eval sample on real IBM Quantum hardware, so the test set is provably not cherry-picked. Verifiable randomness & provenance only — it does not change the model's quality, size, or speed.
Run & re-fuse
Export to Ollama, runs local. When a better base drops, re-apply your profile in one click — your investment compounds.
What can your GPU actually run?
Priced like compliance software, not an API.
On BYO-GPU, fusion runs entirely on your hardware — your data never leaves your building (the compliance play). On the Cloud tier your data goes to our GPU workers for the job (job-based, deleted after) — for teams without that constraint. QPU certification is a +$10 add-on.