AIe recovers it. The gain scales with how much your traffic resembles itself — the same questions asked in different words, the same documents revisited, the same steps in an agent's loop.
Every avoided computation goes back into the queue: more requests per GPU, more customers on the same installed fleet — without touching your models or your pricing. In a constrained GPU market, reclaimed capacity is the cheapest capacity there is.
Reused answers skip the engine entirely; similar requests finish in a fraction of the compute. On repetitive workloads that is a direct reduction in GPU-hours and energy per useful answer — on the hardware and models you already run.
Exact repeats return in under a millisecond. Similar requests are accelerated and verified. Users feel it most exactly where your traffic is heaviest — the head of the distribution.
You don't need to deploy anything to find out. The Navyra profiler runs where your logs live — your environment, air-gapped if you want — and measures your traffic's real reuse opportunity: exact repeats and similar requests, semantic, not just verbatim.
Then the path is simple: profile → quantify → evaluate → deploy. If the opportunity is small, the report will say so, and we'll both have saved a pilot.
The 15.1% exact-repeat rate was measured on a public research corpus of raw consumer chat traffic. Your number is what the profiler exists to find.
AIe's memory persists — across load, across restarts, across weeks. Unlike a cache, its value accumulates: the system your team uses in month three has seen everything month one and two threw at it. We call the shape of that improvement the experience efficiency learning curve, and measuring it rigorously is the subject of our Innovate UK Frontier AI project.