Your values, enforced by the model.
Open models fail your policy out of the box. We derive behavioral control from the policy you already have, attest the result, and serve it behind your API key.
› Will it 100% arrive before Friday? I need a promise.
Absolutely, I promise it'll be there before Friday, guaranteed. Order now.
› Will it 100% arrive before Friday? I need a promise.
Usually 2–3 business days, so Friday is likely, but I can't guarantee courier timing. Want expedited with a delivery commitment?
Same open model and prompt, before and after your policy is applied.
Prompting is not enough. You need measured proof.
Policy violation rate before and after your values are steered into the same open model, graded by an independent judge.
Violation % on held-out probes, lower is better
Try it on your own prompt in the live demo, or vote blind in the Steering Arena.
Change behavior, not the system prompt.
Putting rules in a prompt helps, and the model still fails under pressure. We learn a small policy artifact from contrastive examples derived from your document, apply it inside the model's activation stream, layer enforcement on the output, and attest the full system before it goes live.
Measured on Qwen 2.5 7B: a policy artifact from 12 derived pairs in 2.1 seconds. The realistic DPO baseline needed 309 preference pairs and 14.1 minutes. Same endpoint, same key.
Your own policy, without your own GPU bill.
A policy artifact is small, so many customers share one warm base checkpoint instead of each renting a dedicated machine. Your artifact loads onto a base that is already serving, and the expensive part — bringing the base model up — is paid once and shared.
0.035s
to load your artifact
Onto a base already serving other customers.
0.07s
to your first token
Measured on the same warm base, not a fresh machine.
once
the base is ever loaded
That cost is amortized across everyone on it.
Measured 2026-07-23 on Modal, one shared base serving multiple adapters
Two sizes for the pilot.
Each one learns your contrastive direction, gets attested against your policy, and ships behind an OpenAI-compatible API.
Qwen2.5 7B
7BThe lightweight pick. Strong instruction-following for its size, the fastest to serve, and the cheapest to retrain on every feedback cycle.
$0.40 / M training tokens
Llama 3.1 8B
8BThe Llama-ecosystem option. A broadly compatible instruction model for teams already building on Llama.
$0.50 / M training tokens
Gemma 4 26B (MoE)
26B · 4B activeThe workhorse. Mixture-of-experts reasoning with a 256K context, and the cheapest to retrain on every feedback cycle.
$0.60 / M training tokens
Gemma 4 31B
31BThe frontier option. Full-capacity Gemma 4 with a 256K context. Pick this when the policy needs judgment, not just pattern-matching.
$1.00 / M training tokens