You own the history
License your operating history on your terms.
- You choose sources, period, fields, and exclusions.
- You keep ownership of the underlying records.
- Nothing is licensed without your written approval.
AuraOne / Data Partnerships
Turn approved work history into usable AI data.
Start with the source and intended use. We assess the material, prepare the permitted slice and define the product it can support.
You decide what is included. You retain ownership of the underlying records. No files or system access are needed to start. A named dataset, rights scope, and buyer program are agreed first.
#1042BillingOpen
I cancelled on the 3rd and was charged again on the 5th.
Checking the cancellation date now.
SupportCancellation posted before renewal?
BillingYes. Refund the full cycle.
Account record · edited
- Status
activecancelled- Renewal
$24.00 chargedrefunded- excluded by owner
Accepted
Refund matches policy. Billing reviewer.
One trajectory
Issue → decision → change → accepted outcome
The excluded field stays out.
Three ways to work with AuraOne Data Partnerships
You run AI agents
Your agents do real work. The record of it has value.
- Coding sessions, tool calls, failures, and fixes.
- A partner program. Opt-in only. You choose what is included.
- Nothing is licensed without your written approval.
You build AI
Source records of real work under a signed licence.
- Scoped programs for AI research, training, and evaluation.
- Permitted uses and exclusions are set in each agreement.
- Rights are reviewed before anything is proposed.
AI apps and agent traces
Your agents finish real work every day. Model builders need that record.
Every task leaves a trail. The request. The tools it called. What failed. How it recovered. Whether the work was accepted.
That trail is called a trace. AI teams need traces to train and test better agents. Few have them. You might.
Today a trace partnership starts as a review by our team: business context, then rights, then a sample under NDA. Rebuilding traces as replayable tasks is scoped per program.
This is a voluntary partner program for companies that hold the rights to their own agent traces. Every term is set in a signed agreement. It does not describe how any AuraOne product collects data.
- The task“Customers are being charged twice. Find it. Fix it.”
- It investigatesReads the payment code. Checks the logs. Spots a duplicate transaction.
- It tries a fixChanges the code. Runs the tests. They fail.Test failed
- It corrects itselfReads the error. Fixes the fix. Tests pass.Recovered
- The outcomeA developer approves it. It ships. The double charges stop.Verified
The value is not the final answer. It is the failure, the recovery, and the proof.
Not every trace is equal
- ConversationA question and an answer. Useful, but it does not show the answer was right.
- ExecutionEvery tool call, failure, and retry. How the work was attempted.
- VerifiedThe steps plus proof: a passing test, an approved edit, a confirmed result.
- ReplayableThe task rebuilt as an environment another model can attempt again and again.
What you might already record
- Prompts and tool calls
- Code edits and test runs
- Browser actions
- Failed attempts and retries
- Approvals, rejections, and accepted results
Who this is for
AI apps. Coding tools. Inference gateways. Teams whose engineers work with AI agents. A few months old is fine. Volume and outcomes matter more than age.
How a trace can become a four-part RL task.
A raw session log is only the starting record. In a program that rebuilds traces as tasks, each accepted candidate is structured and checked across four aligned parts before release.
Intent
The target engineering skill, intended behavior, failure mode, and explicit verifier limitations recorded up front.
Prompt
A reconstructed problem statement that states the engineering request plainly without leaking solution diffs or hidden test names.
World
A digest-pinned, network-isolated container and service sidecars that boot identically across repeated cold starts.
Verifier
Hidden fail-to-pass and preserved-behavior checks verified against reference, plausible-wrong, and alternative solutions.
Five separated roles and one calibration loop. A blind adversary among them.
No single model authors and grades its own task. Separated identities across distinct model families triage, author, attack, review, and clear rights—while recurring failure modes compile into outer-loop calibration rules.
Triage
Probes the parent revision against candidate tests and refuses any change that does not fail before the fix.
Author
Writes the four-part task specification, declares checks, and proposes reference, plausible-wrong, and alternative patches.
Blind adversary
Runs on a separate model provider without seeing the reference fix or hidden test bodies, attempting to make wrong patches pass or bypass the grader.
Cold reviewer
Reads Intent, Prompt, World, and Verifier cold on an independent model family and refuses misaligned or under-specified tasks.
Evidence-bound rights
Classifies components by prefix with lockfile and scanner evidence, enforces secret remediation, and records per-provider restrictions.
Outer-loop calibration & audit
Compiles adversary exploits and post-hoc human audit adjudications into versioned calibration rules and a per-release autonomy manifest.
How partners earn
Revenue share
You already hold the rights. We license accepted data and share the proceeds.
Managed contribution
We help you design an opt-in program, set quality gates, and find buyers.
Buyer-funded collection
A model builder pays for a specific dataset or environment, scoped up front.
Value is counted in accepted traces, not raw logs. Every term is set in a signed agreement. Payment is never guaranteed.
We start with a conversation about what you record, which rights you hold, and which outcomes you can show. Nothing moves until you approve exactly what moves.
Explore the opportunity
See what your operating history could become.
Set two numbers for a rough range. A sample review decides the rest.
- No upload
- needed to start
- Your scope
- sources and terms
- One review
- before a proposal
Running an AI app or gateway? This estimator is built for operating companies. Traces are judged on volume and outcomes, not headcount. Explore a trace partnership →
Your inputs
What these inputs can tell you
100 employees · 10 years · United States
Illustrative licensing range
$370,000 – $945,000
Estimated annual licensing range based on organizational scale, archive depth, and domain demand.
Qualification
Completed during scoping review
Rights, connected workflows, outcomes, and buyer demand decide it. A bounded sample review is how we find out.
Business context only. The next form asks for no files, credentials, or customer records. I already sell a dataset
How the range is calculated
The range grows with headcount and, more slowly, with years of history. It is capped and rounded to the nearest $5,000. No other input changes it.
Key factors evaluated during a formal archive valuation:
- Connected workflows with decisions, corrections, and accepted outcomes, not isolated files.
- Rights to license the material, with exclusions honored before delivery.
- Buyer interest in the domain, confirmed during scoping review.
Actual economics depend on rights, buyer demand, workflow continuity, rarity, quality, exclusions, and agreed licensing terms.
Where the history usually lives
- Team chat
- Issue trackers
- Code history
- Support desks
- CRM records
- Shared drives
- Documents
- Spreadsheets
- And more
Kinds of systems, not integrations. The first review needs no connector and no open access.
What buyers review
What makes operating data buyer-ready
File count is not the signal. We look at connected work, visible outcomes, clear provenance, and the rights behind them.
- Workflows & outcomesA connected sequence: the approvals, the corrections, the accepted result.
- Scale & time coverageApproximate task counts, years covered, and how often new work arrives.
- ProvenanceWhich systems, how it was collected, the sample window, and the exceptions.
- Quality & evaluationsThe methods used and the outcome coverage. Verified results appear only when they exist.
- Rights & permitted usesWho holds the source rights, what is excluded, and which model uses are allowed.
- Commercial fitPricing, refresh, and license preferences. All of it is subject to a signed agreement.
Why this history matters
Records of real work show how a task got done.
Operating history holds what someone received, what context they had, which tools they used, what failed, what changed, and what was finally accepted.
crm_export.csvthread.emlnotes_final_v3.docxticket_1042.pdfpolicy.pdf
- ContextThe problem, constraints, and evidenceticket_1042.pdf
- DecisionThe judgment made with that informationthread.eml
- ActionThe tool used or step takencrm_export.csv
- CorrectionWhat changed after a failure or reviewnotes_final_v3.docx
- OutcomeThe approved, resolved, or measured resultpolicy.pdf
Each record comes from work a business actually did.
What you license
Private records of how difficult work gets done.
The decisions, tools, corrections, and outcomes behind your finished work. You set the scope, and you keep the records.
The owner sets the scope.
You choose the sources, period, fields, and exclusions. You retain ownership of the underlying records.
Specialized work, step by step.
Support, engineering, finance, IT, operations, and physical work, with the context and outcome behind each step.
A historical archive can become an ongoing data asset.
Newer work can be licensed later under separately approved refreshes, each scoped on its own.
Recognize the asset
What valuable operating data looks like
The asset is rarely one file. It is the connected history of a task moving from a problem to an accepted outcome.
Software engineering
- Bug report
- Discussion
- Logs reviewed
- Code changed
- Failed test
- Correction
- Code review
- Merged fix
Could supportCoding tasks with hidden checks, private evaluations, and reviewed correction records.
Customer support
- Customer problem
- Initial response
- Investigation
- Escalation
- Attempted solution
- Correction
- Resolution
- Ticket closure
Could supportSupport agents, troubleshooting evaluations, and long-horizon reasoning tasks.
IT and managed services
- Service ticket
- Diagnosis
- Attempted repair
- Failure
- Escalation
- New evidence
- Successful fix
- Confirmation
Could supportIT agents, enterprise troubleshooting, and tool-use training.
Finance and accounting
- Invoice
- Purchase order
- Discrepancy
- Investigation
- Policy check
- Decision
- Approval
- Reconciled ledger
Could supportFinancial reasoning, reconciliation evaluations, and back-office agents.
Sales and revenue operations
- Lead
- Research
- Qualification
- Communications
- CRM updates
- Proposal
- Negotiation
- Outcome
Could supportSales agents, CRM workflows, and research and qualification tasks.
How AuraOne transforms your data
Approved company work, prepared for AI training and evaluation.
We reconstruct approved work, keep the evidence behind it, and prepare it for the AI teams that license it. Our team does this per program.
Real company work
Your operating history
Related evidence distributed across the systems your teams used to complete work.
- Team chat
- Issue trackers
- Code history
- Support desks
- CRM records
- Documents
- Spreadsheets
- Tickets
- Work orders
- Internal applications
TicketTool useCorrectionAccepted outcome
AuraOne
Discover, reconstruct, and verify
- 01Discover
Find high-signal workflows and confirm what can legally and safely be licensed.
- 02Prepare within exclusions
Flag personal information, customer identifiers, credentials, secrets, and excluded fields for removal or transformation under the agreed scope.
- 03Reconstruct
Connect related events across systems into one coherent record of work.
- 04Structure
Organize messy history into context, decisions, actions, corrections, and outcomes.
- 05Verify
Define acceptance criteria, expert reference answers, rubrics, or deterministic checks.
AI-ready assets
Built for training, testing, and improvement
- Training trajectoriesStep-by-step examples showing how skilled people completed real tasks.
- EvaluationsPrivate tasks that test whether an AI system can perform the work successfully.
- Verifiers and rubricsObjective checks or expert standards that determine whether an answer is acceptable.
- RL environmentsReplayable workflows where AI agents attempt tasks, receive feedback, and improve.
The Data Asset Dossier
One asset. Clear coverage, provenance, and status.
A dossier states what an asset holds, where it came from, how it was checked, and what it may be used for. Each fact carries its review state.
Customer Support Resolution Trajectories
- Volume
- 45,000 trajectories (sample specification)
- Coverage
- 4 years
- Modality
- Text and tool events
- Task pattern
- Ticket → investigation → correction → acceptance
- EvaluationNot evaluated
- RightsRights review required
- SampleSample available by approved request
Standardized structure for rights verification, schema documentation, and quality reporting.
How we evaluate
Quality is measurable, not a marketing claim
Every metric carries a definition and a state. A number appears only after a reviewer records the method, the sample, and the result.
| Metric | Definition | State |
|---|---|---|
| Outcome visibility | Share of sampled trajectories with an attributable accepted outcome. | Assessor to verify |
| Trace continuity | Share of sampled trajectories that contain every required stage. | Not evaluated |
| Independent benchmark | Pass rate on a held-out set against a declared baseline, with the method. | Not evaluated |
- Not evaluated
- Supplier reported
- Reviewed
- Independently verified
Supplier statistics stay labeled Supplier reported, however precise they look.
Three partnership programs
Historical archive, recurring refresh, or purpose-built capture.
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.
01
Historical Data
Monetize the work you have already done.
AuraOne identifies bounded, high-value workflows in approved archives and prepares them for AI research, training, and evaluation.
- Team chat
- Issue trackers and code history
- Support desks
- CRM and email
- Documents and spreadsheets
- Work orders and internal systems
02
Continuous Data
Turn operating history into an ongoing data asset.
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.
- New workflows
- New exceptions
- New accepted outcomes
- New correction paths
- New evaluation cases
- Agreed refresh cadence
03
Capture Data
Record valuable work your software never captured.
AuraOne designs structured capture programs for physical and spoken work, including field service, repairs, inspections, logistics, manufacturing, construction, equipment operations, and robotics.
- Video and images
- Audio and expert commentary
- Device signals
- Actions and tool use
- Task context
- Outcomes and corrections
What determines value
Useful records show what happened and why.
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.
- Operating history
- Reasoning
- Corrections
- Accepted outcomes
- Cross-system continuity
- Clean rights
Value is driven by domain rarity, workflow continuity, verified outcomes, clear rights, and active buyer demand. Every archive is reviewed against these criteria to structure a tailored licensing proposal.
Rights, privacy, and control
Nothing moves until you have agreed to exactly what moves.
AuraOne works from bounded, approved scopes, not open access. Sources, exclusions, permitted uses, retention, and delivery terms are set in writing before anything is licensed.
You control the scope
You approve the sources, time period, fields, exclusions, and permitted uses before licensing.
The data remains yours
Licensing does not transfer ownership of the underlying records unless a signed agreement explicitly says otherwise.
Sensitive information can be excluded
The agreement names what stays out: personal information, credentials, account identifiers, secrets, and anything else you exclude.
Every partnership defines
- Defined sources
- Defined period
- Defined fields
- Defined permitted uses
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.
Rights and licensing
Questions owners ask first
Your stated preferences guide the conversation. Every partnership is governed by a bilateral licensing agreement with explicit permitted uses and exclusions.
Who owns the underlying records?
You do. Licensing does not transfer title. The signed agreement defines what AuraOne may do and for how long.
How do you evaluate data without opening our systems?
We start from business context only. If a sample is needed, it comes later, under an NDA, through a transfer path we approve together.
What if the records include client or employee content?
Mark it as restricted. Only material you have the rights to provide, with approved fields and purposes, moves forward.
How is the data priced?
Following initial discovery, our team reviews a bounded sample to prepare a formal archive valuation and licensing structure.
Can we set exclusivity or prohibit model training?
We record your preferences. They become binding only in a signed agreement.
How do updates work?
Each refresh needs an agreed cadence and scope. Nothing connects to your systems on its own.
Do you publish samples?
Public examples are standardized specimens or separately authorized. Access to a protected sample is reviewed first.
Can we change terms or end the partnership?
The agreement sets the term, renewals, termination, and retention. Data already delivered cannot be recalled remotely.
Read the privacy policy, the security page, and pricing.
Signals we commonly look for
Strong fit is about signal—not company age.
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.
- Connected workflows with decisions, corrections, and outcomes
- Proprietary domain or task expertise
- Clear, reviewable rights
- High workflow volume or rare edge cases
- Evidence that connects across systems
- Work that can produce authorized future refreshes
Common candidates include software and B2B SaaS companies, MSPs, support-heavy businesses, financial and professional services, ecommerce, logistics, field services, and industrial operators. Our team evaluates workflow depth and rights clarity across every candidate archive.
Transactions and transitions
Before you archive or shut down the systems, evaluate the data.
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.
From a bounded sample to an approved partnership.
Every stage has a defined source, decision owner, rights boundary, and deliverable.
01
One call, under NDA
Describe the systems, operating history, workflow depth, ownership, and any work that was never captured.
Initial scope and rights context
02
Representative sample review
AuraOne assesses workflow continuity, outcomes, corrections, rarity, exclusions, and potential AI uses.
Bounded sample and opportunity assessment
03
Program and commercial proposal
Historical, continuous, or capture scope is defined with rights, permitted uses, controls, delivery, and economics.
Written scope and proposed terms
04
Preparation and approval
Approved sources are prepared within the agreed exclusions, reconstructed, structured, verified, and presented for the agreed sign-off.
Prepared assets and approval record
05
Delivery or authorized refresh
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
Find out whether your operating history can become a licensable data 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.
I own the data
Tell us what your systems recorded. No files or access needed.
Assess your dataI already sell a datasetI need data
Tell us what the model must learn or prove.
Discuss a data need