Numbers, not claims.

Sub-second end-to-end. Sub-100ms substrate.

End-to-end client-measured latency including TLS, Cloud Run routing, server-side compute, and the round-trip from the caller's machine. Substrate ops (HD KG, analogy, causal) are typically a fraction of network round-trip; the rest is packet flight time.

Same seed, same answer. Every time.

The substrate is deterministic from a seed -- a property no model-in-the-loop architecture can claim. We verify by issuing identical analogy queries 20 times against the live API and comparing outputs.

Calibrated confidence at scale.

Sharded HD knowledge-graph queries return the bound object plus a calibrated confidence. We seed N facts of shape (person, born_in, city), then query each subject and check the returned object matches the originally-bound one.

3,125× cheaper per recall.

Many agent stacks "remember" by re-prepending recall context to every LLM call. That recall slice gets billed at frontier-model input rates per turn. Replacing it with a single records_query shifts the unit cost from per-token to per-call.

Opus 4.7 list pricing $5/M input. Other models differ.

How we measure.

• All measurements run against https://api.neruva.io over public TLS. No localhost, no preferential routing, no server-side shortcuts.
• Each operation is warmed up 10 times to absorb cold-start cost (Cloud Run scale-to-zero adds a one- time penalty on the first call after idle), then measured 100 times.
• Latency is measured client-side via time.perf_counter() around the HTTP call -- the number includes TLS handshake, round-trip from caller location to us-central1, and server-side compute.
• Determinism is verified by comparing 20 reruns of the same analogy query and checking outputs are bit-identical.
• KG accuracy seeds N synthetic facts and queries each subject, comparing the returned object against the originally-bound one.
• Cost ratio uses Opus 4.7 list pricing ($5/M input) for the stuff-into-prompt baseline and our published per-op rate ($2/M for records_query). Other models / cheaper tiers shift the absolute numbers but preserve the order of magnitude.

Reproduce these numbers yourself: clone the repo and run python probes/bench_substrate.py with your own NERUVA_API_KEY. The script is ~250 lines and has no dependencies beyond httpx.

Bring your own analysis.

The full benchmarks JSON is below -- copy it, ingest it, plot the histogram yourself. We update this file every time we run the harness; the timestamp at the top of the page is the last-measured-at.

{
  "base_url": "https://api.neruva.io",
  "namespace": "bench-pending",
  "ts": 0,
  "warmup_n": 10,
  "measure_n": 100,
  "ops": [],
  "side_checks": {
    "determinism": null,
    "kg_accuracy": null,
    "cost_vs_opus47": {
      "records_query_usd_per_call": 0.000002,
      "context_stuffing_opus47_5kb_per_turn_usd": 0.00625,
      "ratio": 3125,
      "notes": "Opus 4.7 list pricing $5/M input. Other models differ."
    }
  },
  "_status": "pending -- numbers will appear after the next benchmark run; see methodology below"
}