AuraOne / Data Partnerships
AuraOne turns approved operating history into lab-ready training trajectories, private evaluations, verifiers, and reinforcement-learning environments—preserving the decisions, tools, corrections, and outcomes that show AI systems how real work gets done.
You decide what is included. You retain ownership of the underlying records.
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At a glance
Why this history matters
Public data can teach models what people know. Private operating history shows them how real work gets done: what someone received, what context they had, which tools they used, what failed, what changed, and what was ultimately accepted.
This is economically useful work—not a synthetic question written solely for a benchmark.
Why now
As models become more capable, AI teams increasingly need proprietary examples of difficult work: the decisions, tools, corrections, and outcomes that never appeared on the public internet.
A recent bankruptcy auction produced a $10 million winning bid from Google and a $7.5 million backup bid from Mercor for de-identified operating data and software, subject to court approval.
Read the public report →Support, engineering, finance, IT, operations, and physical work contain context and outcomes that public documents cannot show.
New work can produce fresh training trajectories, edge cases, corrections, and private evaluations under separately approved refreshes.
Recognize the asset
The asset is rarely one file. It is the connected history of a task moving from a problem to an accepted outcome. Many companies already have thousands of these workflows.
AI useCoding evaluations, debugging trajectories, and software-engineering RL environments.
AI useSupport agents, troubleshooting evaluations, and long-horizon reasoning tasks.
AI useIT agents, enterprise troubleshooting, and tool-use training.
AI useFinancial reasoning, reconciliation evaluations, and back-office agents.
AI useSales agents, CRM workflows, and research and qualification tasks.
How AuraOne transforms your data
AuraOne does not simply clean corporate files and resell them. We reconstruct approved work, preserve the evidence behind it, and build assets that advanced AI teams can train against and measure with.
Real company work
Related evidence distributed across the systems your teams used to complete work.
AuraOne
Find high-signal workflows and confirm what can legally and safely be licensed.
Remove or transform personal information, customer identifiers, credentials, secrets, and excluded fields.
Connect related events across systems into one coherent record of work.
Organize messy history into context, decisions, actions, corrections, and outcomes.
Define acceptance criteria, expert reference answers, rubrics, or deterministic checks.
AI-ready assets
Three partnership programs
Start with the work that already exists, establish an agreed refresh cadence, capture work that was never digitized—or combine the three under clearly defined scopes.
Historical Data
AuraOne identifies bounded, high-value workflows in approved archives and prepares them for AI research, training, and evaluation.
Continuous Data
A historical archive can be paired with monthly, quarterly, or project-based refreshes. Each refresh is separately authorized and can add new edge cases, corrections, outcomes, and evaluation tasks.
Capture Data
AuraOne designs structured capture programs for physical and spoken work, including field service, repairs, inspections, logistics, manufacturing, construction, equipment operations, and robotics.
What determines value
Rarity and quality matter more than raw volume. A smaller connected history with decisions, corrections, and accepted outcomes can be more useful than a large archive of isolated files. Every opportunity is reviewed before any valuation or program is proposed.
The practical equation is rarity × workflow quality × outcome signal × rights × buyer demand. Not every archive qualifies, and every opportunity is reviewed before a valuation or program is proposed.
Rights, privacy, and control
AuraOne works from bounded, approved scopes—not unrestricted access. You can review the prepared material, exclusions, rights, permitted uses, retention terms, and delivery conditions before licensing.
You approve the sources, time period, fields, exclusions, and permitted uses before licensing.
Licensing does not transfer ownership of the underlying records unless a signed agreement explicitly says otherwise.
Personal information, credentials, account identifiers, secrets, and other excluded material can be removed or transformed.
Every partnership defines
Rights matter. AuraOne only licenses data you have the right to provide. Customer-owned, client-owned, or third-party data remains excluded unless appropriate rights and authorization exist.
Signals we commonly look for
A long operating history helps, but uniquely valuable or high-volume workflows can qualify with much less history. The strongest candidates combine proprietary work, defensible rights, meaningful outcomes, and evidence that cannot be reconstructed from public sources.
Common candidates include software and B2B SaaS companies, MSPs, support-heavy businesses, financial and professional services, ecommerce, logistics, field services, and industrial operators. Company type alone does not establish value or eligibility.
Transactions and transitions
Years of operating history may represent a separately licensable AI asset. AuraOne can evaluate it before systems are decommissioned, a business is sold or divested, or historical records are deleted.
For founders, private-equity firms, M&A advisors, restructuring professionals, bankruptcy advisors, and corporate divestiture teams.
Every stage has a defined source, decision owner, rights boundary, and deliverable.
01
Describe the systems, operating history, workflow depth, ownership, and any work that was never captured.
Initial scope and rights context
02
AuraOne assesses workflow continuity, outcomes, corrections, rarity, exclusions, and potential AI uses.
Bounded sample and opportunity assessment
03
Historical, continuous, or capture scope is defined with rights, permitted uses, controls, delivery, and economics.
Written scope and proposed terms
04
Approved sources are de-identified, reconstructed, structured, verified, and presented for the agreed sign-off.
Prepared assets and approval record
05
AI-ready assets are delivered under the agreement. Any later refresh follows its authorized cadence and scope.
Delivery manifest or refresh record
Start with the asset
Tell us what systems you used, how far the history goes, what outcomes exist, and whether the business is operating, transitioning, winding down, or closed.
