Lossless compression, cryptographic signing, and restore from storage instead of re-reading the document.
When a language model reads a long document, the serving engine builds up the model's working state: the attention record of everything read so far. When the session ends, or the process restarts, or the state is evicted under memory pressure, that working state is discarded and the document has to be read again from the beginning.
Revyzor compresses that working state losslessly, signs it, and persists it to your storage tier, so the session can be restored from disk rather than re-read. It runs inside vLLM, SGLang or NVIDIA NIM, on your GPUs, under your keys.
Where this applies: a session still resident in the serving engine's own prefix cache is restored in 39.5 ms on the same hardware, at no cost, and needs nothing from us. Revyzor is for the state that is not resident: evicted under memory pressure, lost to a process restart, or needed again months later.
A retained cache is the model's numeric working state. It is not a human-readable chain of reasoning, and an examiner cannot read it. It does not replace the prompt, the output and the generated reasoning, and it is not what you would hand a regulator in their place.
What it gives you is the ability to put the model back into the state it held, on the same weights, and continue or re-examine from there. The signature covers the retained bytes, so alteration is detectable on reload. That is a narrower and more literal claim than "show the regulator what the model was thinking", and it is the one we can stand behind.
Revyzor attaches at vLLM's documented connector interface (KVConnectorBase_V1), the same extension point used by NVIDIA's KVBM, LMCache and FlexKV, and has been integrated and tested against vLLM, SGLang and NVIDIA NIM. It needs no model changes, no retraining and no application changes, and it runs on your infrastructure, on your models, under your keys, with no dependency on any frontier API provider.
It is not a one-line install. Each engine version has needed integration work, and keeping pace with those engines as their internals change is a substantial part of what we do.
These are sectors where long-document sessions, multi-step agent runs and retention obligations all coincide, which is why we think the workload fits. We have no deployments in any of them, and the oversight regimes listed are context for the sector, not requirements that our product satisfies.
| Sector | Oversight regime | Workload |
|---|---|---|
| Banking | SR 11-7, DORA, EU AI Act | AML investigation, credit decisions, SAR drafting |
| Insurance | Solvency II, EIOPA | Claims processing agents, fraud detection |
| Pharma | FDA 21 CFR Part 11, EMA | Clinical trial analysis, regulatory submissions |
| Legal | SRA, Bar Council | Case preparation, contract review, due diligence |
| Audit | PCAOB, IAASB | Automated audit procedures, working paper review |
| Government | OFFICIAL-SENSITIVE | Intelligence analysis, policy drafting |