NO FILES. NO OBLIGATION.Step 1 of 3 · Your business

What does your company do?

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YOUR INFORMATION

You choose what you share.

Before any data sale

We agree what records may be included and how they may be used. Information you are not entitled to share must stay out. Removing names does not cancel confidentiality agreements or make every record safe to sell.

When you ask for an assessment

NextLevel Data Broker uses your company name, email and the business details you enter to review your enquiry and contact you about an assessment. Requests are stored securely on our server. They are not published on the website, and submitting does not sign you up for marketing.

Do not enter customer identities, bank details, passwords or confidential business information. This form does not ask you to upload files.

Website tools

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When the team contacts you, you can ask about your enquiry, corrections or deletion by replying to them. Continuing use of records already sold is governed by the separate data agreement. Full website privacy notice
NEXTLEVEL.DATA BROKER

Software Product Companies

Turn private engineering history into training-ready tasks.

Your repositories hold years of real engineering work: issues raised, patches written, reviews given and tests that prove the fix. AI labs need exactly this kind of verified, real-world task history.

See examples

No files to upload. No commitment to sell.

WHAT YOUR HISTORY SHOWS

A product engineering team spends its week turning reports into shipped fixes. An issue arrives with a reproduction, an engineer opens a branch, CI goes red, a reviewer pushes back, the patch is revised and the tests go green. After a few years, your repositories hold a detailed record of how your team reasons about code.

That history is what we mean by data here: the records your company keeps while doing its work. Commits, issues, pull requests, review threads and CI runs, mostly never written for anyone outside the team.

Public code mostly shows the final state. Coding models, including the assistants in applications like ChatGPT, also need the path: the bug as reported, the first attempt, the reviewer’s correction and the test that proves the fix. Linked, verifiable chains like that are hard to find in public repositories.

WHAT COULD BE USEFUL

What in your engineering history could be worth money — and why

Issue → pull request → merge, with test evidence

A closed issue linked to its fixing PR, plus a test that failed before and passes after, is a complete task: problem, solution and objective check. A buyer can re-run it for training or evaluation.

Review threads that changed the patch

Reviewer corrections capture senior judgement: the missed edge case, the safer migration, the simpler approach. Threads where the approach changed, not just the formatting, are the most valuable part of most histories.

CI history and reproducible environments

Red-then-green runs prove the fix did something. If builds can still be reproduced through lockfiles or pinned images, each task can be re-verified long after merge. Flaky suites and expired CI logs reduce the value.

Records that are usually less useful

Generated code, vendored dependencies and automated version-bump PRs add volume without judgement. One-word commits with no linked issue, heavily squashed history and untested repositories are hard to turn into verifiable tasks. Forks of open-source projects are usually not yours to offer.

Not every repository can be sold. Ownership, licences, secrets and customer information decide the scope.

WORKED EXAMPLES

Three examples from engineering teams

The three examples below are illustrative. Each is a complete chain — problem, patch, review and verification — which is the unit that matters to buyers.

Example: issue to verified patch

Issue to verified patch

The problem

A customer reported that CSV exports dropped rows containing non-ASCII names. An issue was opened with a reproduction file.

What the team did

An engineer opened a pull request fixing the encoding in the export service and added a failing test. A reviewer asked for the fix to cover the streaming path too; the engineer revised the patch.

The result

CI went from red to green, the PR was merged and the issue was closed with a release note.

Why it’s useful

Issue, patch, review correction and passing tests together form a verifiable task that can train and evaluate coding models.

We check what you own and are allowed to share. Private details and restricted information stay out.

Example: a performance regression

A slow endpoint. A profiled fix.

The problem

p95 latency on /search rose from 180 ms to 1.2 s after a release.

The decision

Engineer profiled it, found an N+1 query and added a join plus an index.

The result

Load test back under 200 ms; regression test added to CI.

Why it’s useful

Issue, profiling evidence, patch and benchmark form a verifiable task. It can train and evaluate models on diagnosing performance problems, not just writing code.

We check what you own and are allowed to share. Private details and restricted information stay out.

Example: a migration with review

A risky schema change. A safe rollout.

The problem

A column rename would break two downstream services.

The decision

Reviewer required an expand-and-contract migration in three PRs.

The result

Rolled out with zero downtime; old column dropped a week later.

Why it’s useful

Review threads that change the approach are rare, high-value training signal. They show how senior engineers reduce risk, not only how code is written.

We check what you own and are allowed to share. Private details and restricted information stay out.

WHAT STAYS PRIVATE

Secrets, customers, clients and licences: what stays out

Secrets

Credentials, API keys, tokens, certificates and environment files are scanned for across the full history, not just the current head; a key deleted years ago still lives in old commits. Anything found is removed before human review.

Customer information

Customer records turn up in fixtures, seed files, logs, test databases and screenshots in issues. These are excluded or replaced with synthetic values. Stripping names is not always enough: a customer-specific config can still point to one account, so some material is left out entirely.

Client-owned and licence-restricted code

If you build for clients, code they own stays out unless the client agrees. We read any confidentiality agreement, a written promise to keep a client’s information secret, often called an NDA, before scoping. Open-source and vendored code is checked against its licence; commercial SDKs and code whose terms restrict redistribution stay out.

People, roadmap and security

Contributor identities are pseudonymised, and pseudonymised data is still treated as personal data. You can exclude repositories, paths or periods that reveal your roadmap, and known unpatched vulnerabilities stay out.

Our AI-assisted check flags possible restrictions: secret patterns, licence headers, client names, personal details. Appropriate people then review rights and privacy. It is not an automated legal approval, and nothing leaves until you approve it.

Your records. Your choice. You approve the package before it is shared.

How we protect your information
TWO WAYS TO EARN

Historical packages and ongoing supply

Two separate offers. You can choose one, both or neither.

SELL EXISTING RECORDS

What selling existing records would look like for an engineering team

What we would ask you

For “Sell existing records”, we would ask for an inventory of repositories, trackers and CI systems, and which you own outright. Which repos are client work, forks or mostly vendored? Do PRs reference issues, and how long does CI keep logs?

What your team does

One engineering lead acts as technical contact: answering questions, confirming scope and arranging read access to the agreed repositories only, on terms settled before anything is copied. A few hours here and there, not a sprint.

What we do

We run secret, licence and personal-information checks, and appropriate people review the results. You get an honest view of what could be worth money and agree the exact scope: repos, paths, date ranges, exclusions. We pseudonymise contributors, rebuild issue-to-merge chains, check that tasks reproduce, and you approve the output.

The offer and payment

You receive a written offer: either a fixed price, or a price that depends on a later sale, always labelled. An early estimate is not an offer. Usually you grant permission to use the agreed records in agreed ways; you keep your code and your rights in it.

Your contract is with NextLevel Data Broker, so you do not negotiate with buyers. Before you agree, we tell you who will receive the records and what they may use them for. Payment follows your contract terms; we do not promise a price or timing before the review. A wound-down company may still sell past history if the rights are clear.

How selling existing records works
EARN FROM NEW RECORDS EACH YEAR

Earn from new records each year: what would change in your engineering process

The second offer, “Earn from new records each year”, is what we call Data Farming. Data Farming simply means keeping better records of the work you already do, so there is more useful information to sell each year. We look at how your team records work today and propose a simple plan.

Small process changes

Link every PR to an issue. Add a PR template asking why the change was made and how it was verified. Give review rejections a reason from a small set: correctness, performance, security, readability. Where practical, commit the failing test first, keep CI logs longer and pin environments.

Each change is agreed with you first. Extra tooling is provided at no charge, but only if it is part of the agreed plan.

What does not change

Your stack, review standards and release pace stay yours. No keystroke logging, nothing switched on by default. Capturing IDE, terminal or screen activity is optional, needs separate approval, and developers are told first; personal apps, passwords and private activity are always left out.

We review what has been kept, you approve, we sell, and you are paid under your agreement. The plan is adjusted each year. Yearly income is only possible while the company is operating, and only if the contract says so. There are clear terms for ending, including removing any tooling, and selling existing records does not sign you up for Data Farming.

How Data Farming works
AN HONEST CHECK

Is this right for your business?

A good fit

Product companies that own their code, with several years of history in a tracker, PRs that reference issues, a real test suite, CI and an active review culture.

Probably not a fit (yet)

Agencies whose code mostly belongs to clients who have not agreed. Very young codebases, repositories that are largely forks, and projects with no tests.

What to have ready

List your repositories, trackers and CI systems, and which you own outright. Flag client-owned, vendored and licence-restricted code. Run a secret scan; we repeat it, but it helps to know early. Nominate one engineering lead as technical contact.

BEFORE YOU DECIDE

Software Product Companies:
common questions.

Straight answers for engineering leaders.

Does the code itself leave our control?

Only the repositories, time range and scope you approve. Delivery terms, permitted uses and any restrictions on redistribution are set in the agreement before anything is prepared.

We build software for clients. Can we include that work?

Only where your contracts give you the right to. Client-owned code is excluded unless the client agrees. We help map which repositories are yours to offer.

Could this expose our product roadmap or a competitive advantage?

You choose the scope, and we can exclude repositories, paths or periods. Removing names alone cannot prevent every possible disclosure, so we agree recipients and uses up front.

Our history contains secrets that leaked years ago. Does that rule us out?

Not necessarily. We scan the full history and remove what we find from the prepared output; you should rotate anything exposed. A repository that cannot be cleaned reliably may be left out.

Is open-source code inside our repositories a problem?

Third-party code is checked against its licence and is usually excluded. Your own code around it may still be in scope.

LET’S FIND OUT WHAT YOU HAVE

Find out what your engineering history could be worth.

Start with a short company assessment.

No files to upload. No commitment to sell.
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