For teams whose AI reads a long document, then waits for a human to review before it acts
The whole document.
Kept, not guessed.
When a long-context session falls out of GPU memory, it is gone. Re-running it does not bring it back: the tools have returned different data since, and the same chain run twice already diverges on its own. Revyzor keeps the model’s actual working state, signs it, and restores it bit-identical hours or months later.
Revyzor is software: a plug-in for the engine that runs the AI model on your own servers. It is not a prompt framework, a set of guidelines, or a chatbot, and it is not a legal application like Harvey or Legora. It is the layer such tools would run on.
Qwen2.5-7B bf16, L40S, local NVMe. For the MLOps reader: restoring beats re-reading by 8.5x at 5,097 tokens and 20.6x at 100,000, because prefill is superlinear and restore is bandwidth-linear. Speed is the reason this is practical, not the reason to do it.
Your premises.·Our plug-in.·No model changes.
Drops into vLLM, SGLang, NVIDIA NIM and AIBrix. No retraining, no fine-tuning, no architectural changes: your model, your hardware, your data.
The session you go back to is not the session you left
Most enterprise AI avoids long documents entirely: RAG guesses which fragments matter, the model answers from those guesses, then forgets everything. Reading the whole document fixes the guessing. It creates a different problem. That working state is large, it does not survive eviction, and re-running the session to get it back does not return the same session: the tools have moved on, and the run diverges from itself anyway.
RAG misses the clause
On the longest contracts in the standard legal RAG benchmark, RAG found the governing clause zero times out of 31.
Re-running is not resuming
We ran the same review chain twice, same hardware, same prompts. 47 of 600 intermediate steps differed and 2 of 200 final answers flipped. On a frontier API it was 11 of 200.
The cache is smaller than you think
An L40S holds about 4.6 documents of 100,000 tokens in its entire GPU cache, shared across every user on the card. A two-hour review is long enough for someone else’s work to evict yours.
The architectural shift
Stop RAG guessing. Keep the context loaded.
Give the model the whole document, once, and keep its working state on disk when the session ends. There are only two routes to an identical session: pin a fully deterministic stack and recompute, which costs roughly half your fleet throughput and still cannot recreate tool outputs that have since changed, or keep the state. Lossless compression is what makes keeping it a line item rather than a project.
RAG
✗Three fragments a search engine guessed. Missed the carve-out on page 47.
Retention
✓The whole agreement, read once. Clause found, record kept.
When this pays, and when it does not
If the session is still resident in GPU memory, you already have the original state and you should use it. Your engine’s own cache is faster than we are, and it is free. We are for after it is evicted: the next day, the other analyst, the pod that restarted, the case reopened six months on. That is the only situation where the choice is between the state itself and an approximation of it.
Measured, not promised
From our controlled study on the standard legal RAG benchmark: whole-document reading tripled exact-clause accuracy overall, and improved it tenfold on merger agreements. Legal contracts are the first published study; the same architecture serves any long-document decision.
Read the full benchmark study·Retention vs RAG, side by side
For your MLOps team: 50/50 LongBench accuracy · 128K context validated · 4 model families · 4 GPU architectures · 3 serving frameworks · full technical detail
What the plug-in does
Compress
1.444x on bf16, checked byte-for-byte at capture. This is storage cost, not GPU capacity: more sessions per terabyte, not more users per card.
Certify
Every checkpoint carries a W3C credential: which model, which organisation, which document, when. Cryptographically signed: change one byte and the signature fails verification.
Persist
Persisted to NVMe. Hours, days or months later the state comes back byte-for-byte as it was left, rather than being rebuilt from a transcript against systems that have since changed.
Attaches at the connector interface in vLLM, SGLang, or NVIDIA NIM. No model changes. Runs on your GPUs. Your data never leaves your environment.
The engagement model: a pilot pattern, not a separate product
Shadow review: runs alongside your existing process, changing nothing
Flag & cite: disagreements raised with document-cited rationale
Human closes: every flag to a named reviewer, decision recorded
Where AI decisions have consequences
Banking—AML, KYC, SARs
Legal—Contracts, M&A, eDiscovery
Telecoms—Complaints, fraud, network ops
Insurance—Claims, underwriting, reporting
Life Sciences—Trials, pharmacovigilance, submissions
Multi-step review—Any workflow that pauses for approval
Any decision that may need to be defended. See the use cases
Find out what your evicted sessions actually lose.
We run Revyzor on your model, your data, your hardware. You see the results before committing to anything.
Or email directly: sales@revyzor.com