More capacity, lower cost and faster answers — from the fleet you already run. Wherever traffic resembles itself — same questions in new words, same documents revisited, same agent steps — AIe converts the repetition into value. We measure yours before you commit.
Many tenants, shared system prompts, agent loops: platform traffic is dense with reuse. AIe raises requests-per-GPU on repetitive tenants — capacity and margin from the fleet you already own, with per-tenant memory isolation built in.
Measured: a 15.1% exact-repeat rate on raw public consumer-chat traffic, before counting similar requests. Indicative — at the consumer-chat mix (15% exact / 25% similar): ~1.6× requests per GPU, ≈38% less GPU time per answer.
The same questions arrive all day in different words. The head of the distribution becomes instant; the long tail runs as normal at ~1% overhead. Latency falls exactly where your users feel it most.
Indicative — at the support-desk mix (30% exact / 40% similar): ~3× requests per GPU, ≈67% less GPU time per answer. Support corpora show high template rates; the profiler quantifies yours.
Agent pipelines multiply inference — and much of it overlaps. Each agent re-processes the same context — the same documents, tool schemas and task state — to understand intent and execute its role; agents hand work to one another in natural language and semi-structured JSON that the next agent parses from scratch. Behind AIe, agents in the same deployment draw on one shared, verified memory: a step one agent has already computed — plan, retrieve, check — returns instantly for the next, and the engine-checked guarantee means a reused step is never a corrupted one. Agent-to-agent communication backed by shared intelligence, not repeated computation.
Indicative — at an agent-loop mix (20% exact / 40% similar): ~2.3× requests per GPU, ≈57% less GPU time per answer. Measured: batched verification accepting sequences up to 11 tokens in one pass.
Precedents, standard clauses, repeated bundles — professional work is built on similarity, and it is also where a silently wrong answer is unacceptable. AIe accelerates the repeat work and refuses the threshold shortcut: near-identical questions with different legal effect are computed, not assumed.
Indicative — at a document-heavy mix (10% exact / 30% similar): ~1.6× requests per GPU, ≈37% less GPU time per answer. Domain profiling programme in progress; results published to evaluation partners first.
For companies building inference engines, desktop AI runtimes or AI platforms, AIe can be embedded as a capability of your product: persistent verified reuse as a differentiating feature of your runtime, improving your customers' economics on your stack. Integration follows the same gateway-plus-adapter shape as our own deployment.
Partnership and OEM conversations under NDA — start here.
Repeated citizen queries, strict data locality, constrained budgets and constrained grid connections. AIe serves more from existing hardware — in your VPC, on your premises, or air-gapped — with per-tenant memory that never crosses a boundary. Navyra holds an Innovate UK Frontier AI Discovery award.
In development: frontier-scale open-weight models on customer premises with per-request verified fidelity. Register interest.
Indicative figures are illustrative estimates, not measurements: computed from measured per-request medians (2,939 ms novel ×1.01 overhead · 214 ms similar · 0.8 ms exact, single stream) under the stated assumed traffic mixes — the same arithmetic as the capacity model. Your mix is what the free profiler measures.
Every row above starts the same way: the free profiler measures your traffic's real reuse opportunity — semantic, not just verbatim — in your environment. Then we evaluate on your traffic in shadow mode before anything touches production.