A Billion Queries, 10 Months, and a Promise Kept
Auspexi • Updated:
1,000,000,000 queries • 72% tokens • 73% latency • 100% big‑model calls avoided
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TL;DR: 1,000,000,000 queries confirmed: 72% fewer tokens, 73% faster total latency, and 100% large‑model calls avoided. This makes AI cheaper, faster, more private, and easier to govern—in defense, healthcare, climate, and beyond.
The human part
It took 10 months of 120‑hour weeks, countless late nights, and the kind of pressure that forces you to choose between giving up and pushing through. I pushed through because I believe AI should be accessible and governable, not just expensive and exclusive.
What this means in the real world
- Defense & public safety: lower‑cost decision support, more on‑device reliability, clear audit trails.
- Medical research & discovery: faster candidate screening, privacy by default, affordable iteration.
- Climate & sustainability: energy‑aware inference, smaller footprints at scale, field‑ready tools.
- Education & access: assistants that work offline and still prove what they did.
- Procurement & governance: signed evidence, calibrated thresholds, fail‑closed posture.
Proof at scale
- Tokens: 72% reduction (102,000,000,000 → 28,999,999,925)
- Latency: 73% reduction (915,000,000,000 ms → 251,000,000,000 ms)
- Large‑model calls: 100% avoided (1,000,000,000 → 0)
How we did it
- Retrieve first: fetch a few relevant facts and pack them efficiently.
- Small‑model first: a small model drafts; heavy tools run only when needed.
- Risk‑aware answers: fetch more or abstain when uncertain, instead of guessing.
- Compact knowledge: anchors and compressed vectors replace raw corpora.
- Evidence by default: signed bundle with metrics, provenance, and crypto profile.
What’s next
Fixed‑scope pilots in customer environments (on‑device or VPC), with measured savings, calibrated thresholds, and a signed evidence bundle at the end.
Start here: plain‑English explainer • Request a Pilot