AIe gives your serving stack a memory. Work your engine has already done is recognised, and previously computed work is skipped — more traffic per GPU, lower latency, lower energy.
Illustrative measured case (Mistral 7B, H100, vLLM) — exact-repeat latency varies by model and prompt length.
Why nothing else does this
The memory is built from your traffic and keeps what your users actually ask, weighted by how often they ask it. Your AI gets more efficient the longer it runs — day 90 beats day 1, and the curve never resets.
One memory for your whole estate — work computed once is reusable by every engine, every GPU, every user. It survives restarts, redeploys and the traffic floods that wipe a prefix cache in a minute.
Every answer served from memory is verified by the engine itself before release, byte-identical to full compute — which is why AIe deploys where similarity caches are banned: finance, government, legal, clinical.
Less compute. Less energy.
Work AIe serves from memory is work your fleet never performs again — and every forward pass that never runs is electricity that never leaves the wall.
Validated results
Inside vLLM · navyra-aie 0.10.0 · baseline is vLLM with KV and prefix caching already enabled. Medians of five runs, validated on NVIDIA H100. AIe attaches to the engine, not the weights or the silicon — so it is not tuned per model, and nothing in it is specific to one GPU class.
Exact repeats are served in about a millisecond, with no configuration. Acceleration of closely-resembling requests activates in supervised mode during onboarding — pilots from August.
Across 362 verified checks on three model families, every accelerated output was byte-identical to running the model in full — not close, identical. A 200-request soak completed the full matrix with zero failures.
A fresh request takes 2,939 ms. The same request served from memory takes 0.8 ms — medians of five runs on Mistral 7B. The engine recognises work it has already done and skips straight to a verified result.
After a 574k-token evicting flood, AIe still answers a repeat in 0.62 ms — where KV-offload tools take 21.3 ms and a built-in prefix cache 60.0 ms. It also recovers 89.5% of its hit rate immediately after a restart.
A two-million-entry index occupies 116 MB and answers in 1.1 ms from RAM, 1.6 ms memory-mapped from NVMe. The memory scales past any single operator's traffic.
The compounding effect
The memory lives outside the engine, in your estate. It converges on the shape of your traffic — what your users ask most is what it keeps. Nothing resets it at midnight, and work computed anywhere in your fleet is reusable everywhere in it. We call the result the Experience Efficiency Learning Curve: hit rate — and savings — grow with tenure while the lookup cost stays flat.
| Request | Latency | vs fresh |
|---|---|---|
| Fresh request — full compute | 2,939 ms | baseline |
| Closely-resembling request | 213.6 ms | 13.8× faster |
| Exact repeat — served from memory | 0.8 ms | ~3,600× faster |
Stock-mode medians of five runs (Mistral 7B, vLLM, validated on H100): one request served fresh, then as a close resemblance, then as an exact repeat. These are three measured points, not a fitted curve — the shape of the curve across your own production traffic is measured per deployment by the Shadow Profiler.
Hit rate rises with tenure — day 90 beats day 1. 2M entries occupy 116 MB and answer in 1.1 ms: the curve is limited by traffic seen, not memory allowed.
Across restarts, redeploys and the eviction floods that empty a prefix cache within a minute of production load. Nothing resets it.
One memory across the fleet — computed once, reused by every engine, every GPU, every user.
Built from your traffic, it transfers to no one. Switching away is not a migration — it is starting the curve again from zero.
Stack position
AIe sits on top of what you already run. Nothing is turned off to make these numbers, and nothing needs to change to install it.
# Add AIe in front of your existing vLLM pip install navyra-aie # One import. Your serving path is unchanged. import navyra_aie navyra_aie.attach(engine, api_key=KEY) # Everything else stays exactly as it was.
AIe runs entirely inside your infrastructure. No request, prompt or output is sent to Navyra. Metering is a local log you export.
Who it is for
AIe earns the most where traffic clusters around a domain and where an approximate answer is not an option. If a wrong answer costs more than the GPU hour that produced it, resemblance-based caching was never available to you. Verified reuse is. Typical estimated savings below reflect how tightly each domain’s traffic clusters — your own figure is measured, free, by the Shadow Profiler.
Thousands of practitioners asking variants of the same questions about the same standards, all day. Client data cannot go to a third-party API, so the model is self-hosted and the GPU bill is yours. Contract and document review is among the most tightly clustered traffic there is.
Runs inside your estate · nothing leaves · no outbound API
On-premise by mandate, air-gapped, and high volume. Administrative and citizen-facing workloads repeat heavily. Byte-identical output is not a preference here — it is the condition of deployment, and it is the one thing a semantic cache cannot offer.
Air-gapped · local metering · no outbound traffic
Regulatory queries, compliance checks and product documentation follow predictable patterns. Banking data stays inside the bank, which means the inference bill does too. A wrong answer is a compliance event, not a bad user experience.
Self-hosted · nothing leaves your infrastructure
The same language patterns, API idioms and error types across thousands of developers. Enterprise assistants self-host for IP reasons, which makes inference cost the primary operational variable and the unit economics existential.
Additive to quantisation · no model changes
Decision support and documentation query the same clinical knowledge base at volume. Patient data sovereignty makes self-hosting non-negotiable, and clinical-adjacent output is not something you approximate to save a GPU hour.
Self-hosted · air-gapped deployment available
GPU cost is the entire cost base. Per-tenant clustering means focused customer workloads compound the gain. Additive to hardware acceleration — H100 plus AIe is not a choice between two things, it is a stack.
vLLM-native · multi-tenant · SDK, not infrastructure
How you start
Three steps, in order. You never pay before you have seen what AIe saves on your own traffic, and the pilot fee comes straight off your first year.
If your annual licence fee exceeds 50% of your verified annual savings, we refund the difference. Worst case, we split the savings 50/50 — and if AIe saves you nothing, it costs you nothing. Savings are computed from AIe’s local metering log, generated inside your estate and exported by you. Your data, not our claim.
In a hurry? Skip the gates and buy the licence today — the guarantee is your evidence, and pilots onboard in signature order. info@navyra.co.uk
Start here
Send us a work email and we’ll set up your free profiler run. Self-hosted vLLM deployments only — if you’re on a third-party API, AIe has nothing to reuse and we’ll say so rather than waste your time.
We'll respond within one business day. Prefer to talk first? info@navyra.co.uk
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