AIe
Navyra AIe — the enterprise inference fabric

AI inference that remembers.

Stop paying your GPUs to compute work they've already done. AIe turns previous inference into future capacity — more traffic served on the GPUs you already own, at lower latency and energy, with verified output.

Profile your workload How it works
● new prompts — full computation● similar prompts — accelerated & verified● repeats — answered from memory
Illustrative, at the consumer-chat mix from the capacity model — the profiler measures your real mix.
<1 msexact-reuse path — a repeated request returns without the engine being called
100%of verified reused answers byte-identical to the engine's own output
~1%overhead on traffic that never repeats — the cost of being wrong about your workload

Measured on NVIDIA H100 with vLLM 0.23 — with the engine's own prefix and KV caching already switched on.

Why now

Inference needs memory.

Models keep getting larger. Agentic systems multiply the number of calls. Inference is becoming the dominant ongoing AI cost — while GPU capacity and grid power stay constrained. Yet serving infrastructure still recomputes substantially similar work, from scratch, all day. The inference stack is missing its fabric. AIe is that fabric.

The fabric

One memory, woven across your serving estate.

Copilots, agents, RAG pipelines and support traffic all draw on — and feed — the same verified memory. Work computed once for one workload returns instantly for every other behind the same fabric: per-tenant, isolated, and growing with use.

Copilots Agents RAG & search Support AIe — the inference fabric one verified memory · per-tenant · grows with use Your engines vLLM · further engines coming Your GPUs the fleet you already own
A prompt computed for one workload returns instantly for any other behind the same fabric. Memory is per-tenant and never shared between customers.
What it does

Three behaviours. One guarantee.

An exact repeat returns from memory in under a millisecond.

A similar request is accelerated, with the output verified before it is returned — byte-identical to what your engine would have produced.

A new request runs exactly as it does today, at about 1% overhead.

Similarity is never trusted on its own. Same computation. Same output. Less work.

New request
2,939 ms
Similar to a prior request
214 ms
Exact repeat
0.8 ms
Response time, Mistral-7B on one H100 (log-scale bars would hide the gap; these are to linear scale, truncated). Same pattern on Qwen2.5-7B and Llama-3.1-8B; the speed-up on similar requests depends on how far into the answer the similarity holds.
The economics

Fit more traffic on the same GPUs.

Serve more

Same fleet, greater throughput. Every avoided computation is capacity returned to the queue — more requests, more users, more revenue from hardware you already own. In a market where the GPUs you have are the GPUs you get, reclaimed capacity is the cheapest capacity there is.

Spend less

Same workload, less compute. Work already done is not billed twice — by your GPUs or your power supply.

Respond faster

Dramatically lower latency wherever work is reusable — instant on repeats, accelerated on similar requests.

Use less energy

Fewer full computations per useful answer. The greenest inference is the one you didn't have to run.

Model your capacity gainSee the value in detail
Who it's for

Built for the people who run inference.

Inference platforms and engines. Enterprise copilots and support. Agentic pipelines. Legal and professional services. Public sector and sovereign deployments — including fully air-gapped. Indicative gains run from ~1.6× to ~3× requests per GPU, depending on how much your traffic repeats.

Solutions by customer type

Don't take our word for it.
Measure your own traffic.

Our free profiler runs where your logs live and tells you how much of your inference is reusable — before you deploy anything. If the opportunity is small, it will say so.

Profile your workloadSee the evidence