Prove the model gave the
same answer last Tuesday.
We stand up SAID against one real production workload in your environment. End state: every governed call produces a replayable, signed Decision Receipt your auditor can verify without us.
SAID in your stack, on real traffic, with receipts.
SAID deployed on one production flow
The deterministic inference engine, sealed into your application or running as a managed client. Your compute, your GPUs, your keys.
Signed Decision Receipts on live traffic
Every governed call writes a signed receipt with inputs, invariants checked, output, and selection hash. Customer-held key. Verifiable offline.
Byte-identical selection replay
For every call, the system reproduces the same selection — byte for byte — on a clean machine months later. The audit theorem in practice, not slideware.
Operator handoff package
Runbooks, monitoring hooks, and an Operation Center instance configured for your environment. Your team owns it after the pilot — we don't hold the keys.
Three sizes. Same end state.
Lower-volume inference flow.
One model, one workload, modest throughput. Stand up determinism evidence end-to-end. Scope: ~4 weeks.
Higher-throughput production flow.
Live customer-facing or internal-decision flow at scale. Real GPU pressure. Scope: ~6 weeks.
Multi-flow inference pipeline.
The whole inference graph. Retrieval + ranking + generation + validation. End-to-end replayability. Scope: ~8 weeks.
On day one after the pilot, your auditor can pull any decision receipt, run the manifest on their own laptop, and reproduce the same bytes. With no call to SMARTHAUS.