The serving stack already has good optimisations. AIe doesn't replace them — it adds the one property none of them have: persistent, verified reuse.
| Prefix / KV caching | Semantic caching | Speculative decoding | Quantisation | Navyra AIe | |
|---|---|---|---|---|---|
| Exact-request reuse | ✓prefix only | ✓ | ✗ | ✗ | ✓<1 ms path |
| Similar-request reuse | ✗ | ✓ | ✗ | ✗ | ✓verified |
| Exact output fidelity | ✓ | ✗threshold decides | ✓ | ✗approximate | ✓verified |
| Persistent memory | ✗evicts under load | ✓ | ✗ | — | ✓grows with use |
| Cross-session | ✗ | ✓ | ✗ | — | ✓ |
| Survives restart | ✗ | ✓ | ✗ | — | ✓89.5% warm |
| Reduces full forward computation | ✓prefill only | ✓on hits | ✗latency, not compute | ✓per-op cost | ✓skipped or shortened |
| Additive to the others | — | ✗competes with AIe | ✓ | ✓ | ✓built to stack |
Benchmarked against vLLM 0.23 with its prefix and KV caching already enabled — AIe's measured gains are on top of a well-configured engine, not instead of one.
Quantisation makes each operation cheaper. Speculative decoding makes each token faster. Prefix caching reuses the prompt you just processed. AIe sits above all three and reuses the work itself — across requests, sessions, and restarts. It is the only layer in the stack that gets better the longer you run it.
Four situations, measured numbers. Bar length is time to answer, drawn to a single linear scale across all four situations.
Per-request times are the measured medians from the Evidence page (Mistral-7B, H100, vLLM 0.23, prefix caching on). "Threshold cache" is the standard behaviour of similarity-threshold caching, not a named product.
Set the traffic mix and see the effect on throughput. Per-request times are measured; the mix is an assumption you control — your real mix is what the free profiler reports.
Q1: "What is the limitation period for a claim in contract?"
Q2: "What is the limitation period for a claim in defamation?"
→ Nearly identical wording.
→ Completely different answers — six years versus one.
A semantic cache sees two sentences 97% alike and serves the stored answer — confidently, silently, wrongly. AIe holds the same stored work — and verifies before anything is returned. Where the questions truly differ, it computes. That distinction is invisible in a benchmark and decisive in production — anywhere a wrong answer costs more than a slow one.
The gain scales with reuse opportunity — and most production traffic resembles itself far more than teams expect. The free profiler measures yours, semantically, before you deploy anything. If the number is small, the report says so.