Compute You Can Cross-Examine

Managed deterministic AI inference at scale: temperature-0, seed-pinned, sha-frozen prompts on a hardware-attested fleet — certified per hardware class against golden outputs before a dollar is billed, with results anchored to a public attestation chain. When the answer has to hold up to a skeptic, "trust us" is not an architecture.

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Currently in delivery: a U.S. legal-corpus determinism engagement

A research client needed over a million units of U.S. federal and state law — public sources — processed by large language models under conditions a hostile expert could verify end to end. The figures below are projections scaled from a completed, inspectable 21,221-unit production run (27.3M tokens, 122,296 verified spans, 0.3% reject rate); final actuals ship with the delivery report.

1,039,000

corpus units of U.S. federal & state law

~1.34B

tokens processed (978M in / 358M out)

~6.0M

verbatim spans extracted, each machine-verified against its source

~3.4B

characters of source text

fabricated spans tolerated — measured false-content rate on the reference run

66 → 12

single-stream days compressed into a ≤12-day delivery window

"1.3 billion tokens of inference where the same input provably produces the same output — certified per hardware class before a dollar was billed."

The properties competitors can't say

Deterministic by contract. Temperature 0, fixed seed, frozen sha-pinned prompt. The same slice run twice on the same backend class produces byte-identical output — and that identity is checked, per class, before anything is billed.

Zero fabrication tolerated. Every extracted span is substring-verified in code against its source text. A span that isn't verbatim is dropped and flagged — never emitted.

Certified before scaled. A 300-unit stratified canary with golden expected outputs gates every hardware class: ≥98% span-set agreement and output-rate bands within ±1.5 points, or that class bills nothing.

Continuously audited. A percentage of persisted output is re-read from disk and re-verified during the run. Runs self-abort on anomaly rates rather than finishing wrong.

Attested end-to-end. Hardware fingerprints, model digests, and prompt hashes ride every run summary; results anchor to a public attestation chain (RustChain), so a third party can verify existence and integrity without trusting either party.

Measured, not promised. Every rate on this page comes from a completed production run whose summary is inspectable — not from a model's self-report.

The workload, in numbers

Figure	Value	Basis
Corpus units processed	1,039,000	contracted scope — U.S. federal + state law, public sources
LLM inference calls	~1.05–1.1M	one per unit + single-retry rule
Model routing	~818K units on a 14B model · ~221K dense units on a 32B model	measured 21% dense share, density-routed
Primary deliverable	~1.8 GB spans data + ~190 MB run ledger	scaled from production run
Wall-clock	~66 single-stream days in a ≤12-day window	fleet parallelism across declared hardware classes

"Six million verbatim legal text extractions, each machine-verified against its source — zero tolerance for fabricated content, measured at zero."

When you need this

Litigation-adjacent data pipelines. Regulatory and compliance corpora. Research that has to replicate. Any AI workload where the deliverable will face an auditor, an opposing expert, or a rules committee — and "the model said so" is not an answer. Elyan Labs runs the inference, certifies the hardware, verifies the output against source, and hands over a provenance package a third party can check with no trust in us at all.

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Figures marked ~ are projections from a measured 21,221-unit production run; final actuals ship with the engagement's delivery report and supersede them. Client identity and contract terms are confidential. Elyan Labs LLC, Louisiana.