Deterministic inference.
Same input. Same governed output.
The model still guesses — SAID governs the guess into one mathematically-governed answer. Ask the same question today or again next year and the governed output is identical, down to the byte, and replayable later to prove it.
Your AI gave one answer in the pilot and a different one in production — and you can't prove which was right.
That's failure F2 — production drift. The model is still probabilistic; the answer you ship is not. SAID turns the model's guessing into a fixed selection, so the same question returns the same governed answer every time — and your auditor can replay it. See the six failures →
Modern LLMs are non-deterministic by design — sampling, temperature, kernel ordering, hardware drift, all conspire to give you a different answer to the same question on different runs. That is incompatible with audit. It is incompatible with replay. It is incompatible with the idea that policy applies.
SAID is the inference layer that closes the gap: a deterministic constraint gate that turns probabilistic generation into a selection process whose result is byte-identical on replay. Your auditor asks “what would the model have answered last Tuesday?” — SAID gives them the same bytes.
SAID · Deterministic selection
Three places SAID earns its keep.
Re-run a credit decision in discovery.
Counsel asks what the model decided last quarter. SAID returns the same mathematically-governed answer, byte-for-byte — replayable on their machine, without us.
Defend an automated triage call.
An adjuster's model gets audited. SAID replays the governed decision exactly, so the audit sees what actually ran — not a reconstruction.
Make a suggestion reproducible.
A diagnostic recommendation has to land in the record the same way every time. SAID pins the governed output so the same input always yields the same answer.
What SAID actually does, in a list.
Constrains generation at the kernel boundary
SAID is not a wrapper around the model. It is a deterministic constraint gate that sits inside the inference path. The probabilistic part runs; the answer that crosses the line is selected.
Produces byte-identical selection on replay
Same prompt, same rule snapshot, same runtime version — the bytes that come out of SAID are the bytes that came out last time. The auditor proves it on their own machine.
Captures determinism evidence
Every inference writes the kernel ordering, the sampling seed, the constraint gate state, and the selection hash into the receipt. Determinism stops being an aspiration and becomes a property.
Runs on the GPUs you already have
GPU-first, your hardware. SAID does not call out to a SMARTHAUS inference cloud. Your prompts, your weights, your bytes — stay on your compute.
Composes under UCP
When SAID runs under UCP, the constraint gate is bound to UCP's invariant set. Determinism is one property of the governed call; UCP enforces the rest.
Backed by a written audit theorem
The replay property is not a marketing claim. It is a theorem with named lemmas, in the same Mathematical Autopsy framework that shipped the diagnosis. See the proof.
One runtime. The same four surfaces. Inference is everywhere.
SAID ships through the same federated surface model as UCP and MAE — one runtime, four shapes, identical determinism evidence wherever it lands.
Inside your inference path
Linked into the service that calls the model. The constraint gate sits between your prompt and the bytes you ship.
Desktop, unmanaged
For practitioners running deterministic inference on their own GPU, against their own workloads. Full agency, no org overlay.
Same app, org-managed
Determinism evidence reports up to Operation Center. Policy pushed down. Auditors see one fleet, one set of receipts.
Voice-led, on the phone
Native iOS / Android. Hands-free dictation, conversation, library. The same SAID engine, mobile-shaped.