Data infrastructure for AI training

Clean data. Smarter AI.

Pureset sources, curates and validates high-integrity data designed specifically for AI training — free from noise, bias and duplication — so your models generalize better, converge faster and ship safer.

Every dataset ships with a quality report, a lineage ledger and rights documentation.

pureset inspector · corpus: ps-enterprise-2021 Verified

Quarterly filing corpus · 2009–2021

Pre-2022Enterprise-clearEnglish · 4 locales
Source archivePublic regulatory filings · public record
Captured2019-11-04 · pre-synthetic
Chain of custody4 hand-offs · sealed
Duplicates pruned1.28% of tokens
Token count812.4M
License classEnterprise-clear · indemnified
Provenance stamp · SHA-256
3a7f9c02e1b84d55f0c9a6e21b…
ps-inspector v3.2 · read-only preview Quality report
4.2 PBverified corpus under management
1,900+archival sources under license
38Mdocuments with sealed provenance
100%human-authored, provenance-documented records

Built for AI teams in

  • Frontier AI labs
  • Financial services
  • Healthcare
  • Legal
  • Public research
  • Enterprise software
Why now · The synthetic cliff

Synthetic content is flooding the internet.

When models learn from model output, diversity decays with every generation. Finding truly representative, trustworthy data has become a growing challenge — and the answer isn't more scraping. It's data with a verifiable human origin.

Web-scraped corpus with synthetic content

Collapse risk · High
100755025 G1G2G3G4G5

Distinct human signal retained per training generation · illustrative

  • Self-referential loops compound with every generation
  • Duplicates and near-duplicates inflate token counts
  • Provenance is unknowable after the first scrape

Pureset human-verified lineage

Collapse risk · Low — human-authored
100755025 G1G2G3G4G5

Lineage-stable corpus, same measurement · illustrative

  • Human-authored at the source: pre-synthetic archives or attested expert work
  • Signed chain-of-custody from archive to training shard
  • Deduplication and purity verified by independent audit
Web-scraped corpora compared with Pureset verified lineage
DimensionWeb-scraped with synthetic contentPureset verified lineage
Median duplicate rate31.7% of tokens0.9% after pruning
Provenance coverage< 12%, largely inferred100% cryptographically signed
Temporal certaintyCrawl-date approximationsDocument-precise capture dates
Benchmark contaminationUnmeasured, compounds each generationScreened against public benchmarks
Legal defensibilityWeak, undocumentedRights-cleared, indemnified

Charts are illustrative. Model collapse under recursive training is documented in Shumailov et al., “AI models collapse when trained on recursively generated data,” Nature 631, 755–759 (2024).

How Pureset works

Source. Curate. Validate.

Clean data isn't found — it's engineered. Every Pureset dataset passes three accountable steps before it reaches your training run.

See the full pipeline
01 · SOURCE

A verifiable human origin

We start where the paper trail still exists.

  • Licensed archives and pre-2022 captures that predate the synthetic flood
  • Commissioned expert data with human-authorship attestation
  • Rights cleared and documented before ingestion

Better generalization from truly representative data

02 · CURATE

Noise, bias and duplication removed

Deterministic pruning that keeps rare human signal.

  • Exact and near-duplicate removal with MinHash LSH
  • Quality and perplexity filters tuned per domain
  • Source, era and demographic rebalancing

Faster convergence: no compute spent relearning duplicates

03 · VALIDATE

Every claim measured and sealed

Numbers you can check — not adjectives.

  • Contamination screening against public benchmarks
  • PII scrubbing with human-audited samples
  • Cryptographic lineage ledger for every shard

Safer outcomes you can defend in an audit

Evidence, not adjectives

See what “clean” means — in numbers.

Every Pureset release ships with a Data Quality Report: a purity score, residual duplication, bias distribution, contamination screening, PII audit results and license class — measured, and re-verifiable with your own tooling.

data quality report · ps-stem-2021 · release 3.2 Passed
Purity score

Composite of six measured dimensions across 28.4M documents.

Duplication0.9% residual near-dups99
Contamination0.02% n-gram overlap99
Bias balancewithin ±5% of baseline93
ledger seal LS-77190sample · values illustrative
Trust & governance

Built to pass procurement.

Audited controls, documented rights and contractual guarantees that survive due diligence.

Trust & governance

SOC 2 Type II

Audited controls across security, availability and confidentiality.

Report available under NDA

ISO/IEC 27001

Certified information security management across ingestion, storage and delivery.

Certificate IS-XXXXXX

Chain-of-custody

A signed provenance ledger accompanies every shard, with notarized hand-offs.

100% record coverage

Legal indemnification

Enterprise licenses include infringement indemnification and rights documentation.

Included in enterprise terms

Clean data. Smarter AI.

Tell us where your model is headed. We'll scope the corpus, share the private catalog under NDA and ship a pilot slice with a full validation report.