Problem↓Decision↓Outcome ✓
Everyday work.
A support issue resolved. A delivery rerouted. A process improved.
The next generation of AI needs more than public information. It needs real examples of how work gets done.
Amazon
You solve problems.
AI learns from the experience.
The value is in how the work gets done.
Customer: ••••••Company: ••••••Useful steps preserved ✓
Useful, anonymized examples.
We prepare records to remove identifying details and keep the lessons that matter.
✳Context → action → result
Experience AI can learn from.
AI teams use approved examples to train and test how their models handle real tasks.
It’s about the pattern behind the work — not a specific customer.
Your experience. Your boundaries.
Start with a specific workflow or collection of records. We assess buyer fit, review the rights available and agree what can be included before preparing an evaluation sample.
No passwords
No full-system access
No automatic upload
You approve the scope
Find the useful experience.
We identify which parts of your business experience may be useful for training AI.
Set the boundaries.
A package can be limited to one workflow, team, time period or record type.
Remove identifying details.
Our preparation process includes anonymization and excluding sensitive records. The focus is on workflows, not individual customers.
Nothing moves without approval.
The buyer, permitted use, scope and terms are defined before anything is delivered.
You stay in control.
Checking eligibility does not give us access to your company or your systems.
No passwords
We do not ask for company passwords during qualification.
No system access
We do not connect to your tools just because you apply.
No files required
The first step is simply understanding your business.
Nothing without approval
You decide whether any future opportunity moves forward.
You probably have questions.
The simple version: first we learn about your company. Nothing gets uploaded or licensed just because you fill out the form.
Why is this market possible now?
The first generation of AI learned heavily from public internet data. Newer AI systems also need high-quality, real-world examples of how people make decisions, use software, solve problems and complete work. Much of that information only exists inside operating businesses.
Why can’t AI companies just use the public internet?
The internet contains a lot of information about what businesses do. It contains far fewer complete examples of how work actually happens inside a company, from the original problem through the decisions, actions and final outcome.
What would an AI company actually use this data for?
Depending on the buyer and agreement, real-world business data can be used to improve model training, post-training, evaluation and simulated work environments. The goal is often to help AI become better at tasks performed by real employees.
Are they buying my trade secrets?
That should not be the goal of a properly structured deal. The useful signal is often the workflow itself: how a problem was handled, what context mattered and what outcome followed. Sensitive information, proprietary secrets and records outside the agreed scope can be excluded.
Does this mean a buyer gets access to my entire company?
No. A licensing opportunity can be scoped to specific records, workflows, departments or time periods. Qualification does not give anyone access to your systems.
Do I lose ownership of my data?
Not necessarily. These opportunities can be structured as licenses where the company keeps ownership while granting defined rights for a specific permitted use. The actual agreement should spell out those rights clearly.
What happens to customer names and private information?
Personal, identifying or otherwise sensitive information should be reviewed before delivery. Depending on the dataset and deal, it may be removed, de-identified or excluded entirely.
Could this help a competitor copy my business?
The agreement should define the buyer, permitted use and scope. The goal is to license useful training signal, not to hand another operating company your confidential playbook. Any material competitive or confidentiality risk should be reviewed before a deal is approved.
Why would I monetize this instead of leaving it alone?
Your company has already paid to create years of operational history through salaries, software, customer support and day-to-day work. If part of that history has legitimate buyer demand, licensing can turn an otherwise unused archive into a new business asset.
Do I upload anything when I apply?
No. The qualification form only asks about your company, your history and the types of systems and records you have.
Am I agreeing to license anything?
No. Applying only tells us you are interested in seeing whether there may be an opportunity. You can review the buyer, scope and terms before deciding whether to proceed.
How much could my company’s data be worth?
It depends on the type of data, its history, volume, quality, uniqueness, buyer demand and the rights available. We first determine whether there is a fit before discussing a potential transaction.
Established business.
Untapped potential.
Years of operating experience could become your next revenue opportunity.
20+
Employees
An established team with real business workflows.
3+
Years operating
A history of decisions, processes and outcomes.
Digital
Operational records
Documents, support histories, CRM records or project knowledge.
Authority
To explore a license
An owner or authorized decision-maker who can review the rights available.
Explore the earning potential.
The opportunity depends on what your business knows — and what an AI buyer needs.
$100KPotential licensing valueto$2MPotential licensing value
$100K$500K$1M$2M