Reserve Data
A new return on old work

Make millions
from your
old data.

Your Slack threads, emails and project records capture how real work gets done. AI developers pay for that context. We help you explore what yours could be worth, with personal and confidential information addressed before licensing.

Discuss your data ↗See what qualifies

Start with a conversation. No raw data required.
Value depends on buyer demand, data quality and licensing rights.

The work behind the workInternal conversationsProject historiesTechnical decisionsOperational knowledge
01 What could be valuable

Everyday work.
Extraordinary context.

The value is often in the back-and-forth: the question, the false start, the decision, and the result. Here are the kinds of records worth taking a closer look at.

01 / Slack threads · internal email

Conversations & decisions

A team debates a delayed launch, compares two options, and records why one won.

What an AI developer could learnReasoning, trade-offs, and decisions in context.
02 / Support tickets · help-desk notes

Support & resolution

A reported issue, the questions asked, the steps tried, and the fix that worked.

What an AI developer could learnHow to diagnose a problem and reach a resolution.
03 / Project plans · tasks · handovers

Projects & delivery

A brief becomes a plan, changes after feedback, and ends with a documented result.

What an AI developer could learnPlanning, sequencing, and adapting to constraints.
04 / Issue histories · code reviews · incident reports

Engineering & systems

A bug report connects to an investigation, a reviewed change, and a passing test.

What an AI developer could learnTechnical reasoning tied to a verifiable outcome.
05 / Runbooks · dispatch logs · internal procedures

Operations & exceptions

A shipment misses a handoff. The team spots the exception and finds a workable route.

What an AI developer could learnHow real operations handle the unexpected.
06 / CRM notes · proposals · account handovers

Commercial workflows

A customer requirement becomes a scoped proposal, with objections and revisions along the way.

What an AI developer could learnHow requirements are clarified and work is scoped.

Illustrative uses, not a list of approved datasets. Buyer demand, ownership, permissions, and the sensitivity of the records determine what is suitable.

02 Privacy, made concrete

Your conversations.
Without the identities.

The ideas, decisions, and problem-solving are what make your records useful. Names, contact details, and identifying references are marked for removal before a dataset is considered for licensing.

project-discussionIllustrative team conversation
IDENTIFIERS REMOVED

The team at asked if we can move the rollout forward. Their contact is .

I’d keep the first release small. We can catch issues before opening it up to everyone.

Agreed. I checked account . The trial went well, but we haven’t tested the full load yet.

Let’s start with a small cohort, watch the failure rate, and expand once it’s stable.

That works. I’ll update the plan and schedule a review after the first batch.

Boxed out: names, company references, email addresses, and account IDs.

Fictional examples illustrating data preparation. Actual records require a defined preparation and review process. Replacing identifiers does not by itself make a record anonymous; context and combinations of details also need review.

01 / SET THE BOUNDARY

Choose what’s in scope.

Define systems, date ranges, and record types first. Exclude private channels, HR records, credentials, and restricted material from the proposed dataset.

02 / REVIEW THE DETAILS

Look beyond names.

Check contact information, customer references, attachments, metadata, and details that could identify someone indirectly. Remove or exclude material that cannot be appropriately prepared.

03 / CONTROL THE RELEASE

Approve the scope and use.

Agree the buyer, permitted uses, exclusions, and review responsibilities before transfer. De-identification does not replace permission to license the underlying records.

03 Why context matters

The answer is useful.
The path to it is valuable.

A finished document tells part of the story. The record of how people reached it can reveal much more.

01 / THE START

A request

A project needs a change.

02 / THE CONTEXT

A discussion

The team weighs the options.

03 / THE WORK

A revision

The plan evolves with feedback.

04 / THE REASONING

A decision

A direction is chosen.

05 / THE RESULT

An outcome

The team sees what worked.

Illustrative workflow. Actual suitability is assessed with the buyer.
04 Our process

From stored records
to a real opportunity.

A focused assessment first. A clearly defined agreement before any data changes hands.

01

Find the fit.

Tell us which systems you use, how far the records go back and what kinds of work they capture.

02

Assess the value.

Explore buyer interest alongside data quality, ownership and any restrictions on use.

03

Set the terms.

Define the records, permitted uses, exclusions and commercial terms with the relevant parties.

04

Prepare the data.

Agree how sensitive information will be handled, then coordinate preparation and transfer after approvals.

Start with an inventory. Not an upload.

We can begin with systems, dates and record types. Keep confidential files in your environment while we assess the opportunity.

05 A few good questions

Before we talk.

What could our data be worth?

There is no universal price. Buyers assess usefulness, depth, quality, exclusivity and licensing rights. The headline describes an opportunity—not a quote or a guaranteed payout. An assessment starts with what you actually have.

What happens to names, emails, and other personal details?

The preparation plan should identify personal information for removal or replacement and exclude records that are not suitable to share. That includes more than names: email addresses, phone numbers, account IDs, attachments, and identifying context all need consideration. The examples above show the idea; each actual dataset needs its own assessment and review.

What about confidential business information?

Customer identities, negotiated pricing, trade secrets, access credentials, and restricted third-party material need explicit treatment too. Define exclusions before preparation and agree what may remain in the proposed dataset. A company’s possession of a record does not automatically mean it has the right to license it.

Can scrubbing guarantee that nobody is identifiable?

No blanket guarantee would be responsible. A role, location, event, or combination of details may identify someone even after names are removed. Review must consider that context; some records may need further generalization or exclusion.

Do we have to send you files first?

No. The first conversation can cover your systems, archive history and record types. Do not send raw Slack exports, emails or confidential files at this stage.

Who buys this kind of data?

AI developers may use suitable licensed data for training and evaluation. Buyer interest and requirements vary by dataset. We discuss the proposed buyer and intended use as part of a specific opportunity.

Who needs to be involved on our side?

A business decision-maker and someone familiar with the records are a useful starting point. Legal, privacy and security stakeholders may also need to review the proposed scope and terms.

Let's take a look

Put your old data
to work.

A conversation about what you have could be the start of a new revenue stream.

Contact details coming next

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