The verification and audit infrastructure layer for AI. Fresh evaluation tasks, objective rewards, calibrated uncertainty, and signed reports. So every model claim can be tested, reproduced, and trusted.
25 evaluation envs6 capability domainssigned receiptsself-hosted or VPCApache-2.0 SDK
Verification protocol validated across five model families
Every evaluation task is generated fresh from a parametric prior. No memorisation. No leakage. No replays.
Self-hosted or VPC. Founder-led integration into your training, procurement, and release-gate workflows.
0 leaked benchmarks
every task procedurally generated · contamination structurally impossible
Calibration and verification calls, last six months. Every run is a traced, signed receipt, visible to whoever holds the API key.
Static benchmarks give you a score. We give you evidence.
AI teams don't need another scoreboard. They need reproducible reports their post-training leads, procurement teams, and compliance reviewers can verify, across six capability domains.
| Verifiable Labsverification infrastructure | Static benchmarksfrozen scoreboards | Human-label vendorslabelled training data | Generic eval harnessrun-your-own evals | Internal PDFsone-off reports | |
|---|---|---|---|---|---|
| Coverage | |||||
| Live evaluation environments | 25 | Frozen | Custom-built | Plug your own | Ad hoc |
| Capability domains covered | 6 | 1–2 | 1–2 | 1–3 | 1 |
| Evidence quality | |||||
| Calibrated uncertainty on every reward | |||||
| Contamination-resistant by construction | |||||
| Frontier-model baselines on every task | |||||
| Procurement & release gates | |||||
| Signed audit receipts (X.509) | |||||
| Reproducible reports linked to every seed | |||||
| Open-source SDK (Apache-2.0) | |||||
Competitor data based on public marketing as of 2026. Corrections welcome via GitHub issues.
Signed model reports in minutes.
vlabs-audit turns any model evaluation into a reproducible, signed PDF. Every chart, score, seed, and trace links back to source evidence. Export for procurement, safety review, investor diligence, or internal release gates.
From audits to training signals.
Every Verifiable Labs run produces more than a report. Fresh task instances, objective rewards, calibrated uncertainty, paired traces, and failure-mode taxonomy. Use it first to verify model claims. Then plug the same reward layer into your post-training workflows to improve models with evidence you can trust.
The first product is audit-grade verification. The platform is reward infrastructure for post-training. Same protocol, same calibration, same traces. Procurement signs the receipt; your training loop consumes the reward.
One CLI. Every model. Calibrated by default.
Fast install, clean API, local-first. Drop the same verifiable audit call into any post-training pipeline or CI release gate. No account required. Same trace shape across all 25 environments.
- math
- code
- sql
- tools
- long-context
- imaging
Six capability domains. One interface.
Math reasoning, code execution, SQL execution, tool orchestration, long-context reasoning, scientific reconstruction. Same verifiable audit call, same trace shape, same calibrated reward semantics. Switch capability domains by changing one flag.
Bounded evidence on every reward.
Know when a model is actually better, not just noisier. Every score ships with calibrated uncertainty. When we say “0.488”, we also tell you true reward ∈ [0.446, 0.530] with 90 % probability. Make release decisions on bounded evidence, not point estimates.
Buyer questions, answered straight.
What customers, procurement teams, and investors ask before signing. Missing one? Email [email protected]. We'll add it.
Replace your scoreboard with evidence.
Run a signed audit in five minutes. Open-source SDK, hosted, or self-hosted in your VPC.