Proprietary training data
We acquire operational records, internal logs, and decision trails from shuttered businesses — then license them as clean, structured datasets to the labs building frontier reasoning models.
Join the waitlist01
We identify distressed businesses and shuttered companies before their operational data is lost. Acquisitions come through founders, receivers, and estate handlers — not crawlers.
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Raw records are cleaned, deduplicated, and formatted for training pipelines. PII removed where required. Delivered as JSONL, Parquet, or via direct API access.
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Direct data agreement with defined scope. Clear provenance, no public benchmark contamination. Signal your next model needs that isn't already on the open web.
Labs building models that need to understand how real businesses operate — not how they describe themselves in press releases.
Multi-step decision logs and real enterprise planning trails. Synthetic data can't replicate how organizations debate, revise, and execute under constraint — these records do.
Building models that handle CRM workflows, procurement, or financial ops? The missing signal is what real business operations look like when things go wrong.
Targeted acquisitions across logistics, SaaS, retail, and professional services. Rare enough to matter, structured enough to load immediately.
Every dataset comes from a direct acquisition — founders, receivers, or estate handlers, not automated crawls. That means clear provenance, no benchmark contamination, and signal that isn't already in your base model.
Direct acquisition
Sourced from founders and receivers, not automated scrapes.
No benchmark overlap
None of this data appears in standard evals or public training sets.
Exclusive licensing
Primary licensee gets exclusivity. No resale, no concurrent labs.
Structured on delivery
Clean JSONL or Parquet, ready to load into your training pipeline.
We're in active acquisition. First-access licenses go to labs on the waitlist. We'll reach out when a dataset matches your domain — no noise in between.