Guides to licensing data for AI
Plain-English answers to the questions business owners ask before sharing data with AI developers — value, privacy, law, and contracts.
How to Sell Your Company's Data for AI Training
Many companies are sitting on years of operational records that AI developers cannot find on the public internet: code history, support tickets, internal documents and the reasoning behind real decisions. Selling or licensing that data can create a new revenue stream, but only if you approach it in the right order. This guide walks through the practical steps, from working out what you hold to signing a contract and transferring data safely.
Read guide →How Much Is My Company's Data Worth for AI Training?
There is no public price list for company data, and anyone who quotes a number before looking at your data is guessing. What we can explain is how value is judged. This guide covers the factors that make one dataset worth far more than another, and what you can do to understand where your own data sits.
Read guide →How Much Is a Codebase Worth for AI Training?
The value of a private codebase for AI training is rarely in the source code alone. It is in everything around it: the planning documents, the issues, the commits, the review discussions and the tests that prove the code works. This guide explains how codebases are judged and what you can check in your own repositories.
Read guide →Selling Source Code to AI Companies: What to Know First
If your company has years of private code, AI developers may be interested in using it to train coding models. Before you agree to anything, it is worth understanding what you are actually giving, what you keep, and what to check. This guide covers the practical and legal questions to work through first.
Read guide →Data Licensing vs Selling Data: Which Is Right for You?
When a company decides to make its data available for AI training, one of the first choices is how. You can license it, sell it outright, or work on a custom project. Each model gives a different balance of control, exclusivity and effort, and the right one depends on your goals rather than on any general rule.
Read guide →What Makes Data Valuable for AI Training?
Not all data is equally useful for training AI models. The data that matters most today records real work from start to finish: what someone was trying to do, how they went about it, and whether it worked. This guide explains the qualities AI developers look for and how to recognise them in your own systems.
Read guide →Data Anonymization for AI Training: A Practical Guide
Before company data can be used to train AI models, sensitive information has to be dealt with. Anonymization is the process of removing or replacing details that could identify people, customers or secrets, while keeping the parts of the data that make it useful. This guide explains how it works in practice and where it commonly goes wrong.
Read guide →De-identification vs Anonymization: What's the Difference?
De-identification, pseudonymization and anonymization are often used as if they mean the same thing. They do not, and the difference matters because privacy laws treat them differently. This guide explains each term in plain English and what it means for a company sharing data for AI training.
Read guide →NDA Checklist for Sharing AI Training Data
Before any sample of your data is reviewed by a potential partner, you should have a non-disclosure agreement in place that sets clear boundaries. A standard sales NDA is often not enough for data. This checklist covers the points worth checking, so you can review a draft with confidence and know what to raise with your lawyer.
Read guide →Is It Legal to Sell Company Data for AI Training?
In many cases a company can lawfully sell or license its own data for AI training, but whether a particular deal is legal depends on what the data is, who owns it, what promises were made about it and which laws apply. This guide sets out the main questions to work through. It is general information, not legal advice.
Read guide →GDPR and AI Training Data: What Companies Need to Know
If your company data includes information about people in the EU or UK, the General Data Protection Regulation (GDPR) is likely to apply when you share it for AI training. This guide explains the parts of GDPR that matter most and how anonymization fits in. It is general information, not legal advice.
Read guide →India's DPDP Act and AI Training Data: A Guide for Businesses
Indian companies considering sharing operational data for AI training need to understand the Digital Personal Data Protection Act 2023 (the DPDP Act). It sets out how digital personal data may be processed, what consent looks like and what obligations fall on businesses. This guide covers the parts most relevant to data partnerships. It is general information, not legal advice.
Read guide →HIPAA and AI Training Data: De-identification Explained
Healthcare organisations hold some of the most specialised data there is, but in the United States much of it is protected under HIPAA. HIPAA does not ban the use of health data for AI training, but it sets strict rules, and de-identification is the main route by which such data can be shared. This guide explains how it works. It is general information, not legal advice.
Read guide →Client-Owned Data and IP: What You Can and Cannot Include in an AI Data Deal
Many service businesses hold large amounts of data that technically belongs to someone else: code written for clients, documents prepared under contract, tickets raised by customers. Before any of it goes into an AI data partnership, you need a clear answer to one question: who owns this, and what did we agree to? This guide explains how to work through that question in a practical way.
Read guide →What Are AI Data Partnerships, and How Do They Work?
An AI data partnership is an agreement in which a company provides some of its real operational data, under strict conditions, to help train or evaluate AI models, and is paid for it. This guide explains what that means in practice, what kinds of data are involved, the usual deal structures, and what a careful process looks like from first conversation to transfer.
Read guide →Why Do AI Labs Need Real Enterprise Data?
AI developers are no longer only building chatbots that answer questions. They are building systems that write software, operate business tools and carry out multi-step tasks. Teaching that kind of work requires examples of how it is really done inside companies, and those examples are mostly locked inside private systems. This guide explains the gap and why your records can help fill it.
Read guide →What Training Data Do AI Coding Agents Need?
AI coding agents are systems that take a software task, such as fixing a bug or adding a feature, and work through it by reading code, editing files and running tests. Training them well takes examples of real software development from start to finish. This guide explains what that data looks like and why private engineering history is often more useful than code alone.
Read guide →What Data Helps Train Computer-Use AI Agents?
A computer-use agent is an AI system that operates software the way a person does: it looks at the screen, clicks, types and moves between applications to complete a task. Teaching that requires examples of real people using real business software to get real work done. This guide explains what that data looks like, why it is scarce, and how to approach sharing it responsibly.
Read guide →What Is Workflow Data, and Why Do AI Agents Need It?
Workflow data is the record of how work actually moves through a business: who started a task, which systems it touched, what decisions were made and how it ended. AI agents are being built to carry out exactly these kinds of tasks, and they learn from examples of them. This guide explains what counts as workflow data, what makes it useful, and how to think about sharing it.
Read guide →How Real Enterprise Workflows Become RL Environments for AI
Reinforcement learning, or RL, is one of the main ways AI agents are trained to complete multi-step tasks. It relies on environments: practice settings where an agent attempts a task and gets told whether it succeeded. This guide explains what an RL environment is in plain terms, and why real enterprise workflows are a strong source of realistic ones.
Read guide →How to Get Value From a Shut-Down Startup's Codebase and Records
When a startup closes, its code and records usually sit untouched in a cloud account until someone deletes them. But the full history of building a product, from early plans to the final release, is the kind of material AI developers find useful. This guide covers what to check, what to keep, and how to approach a data partnership during a wind-down.
Read guide →How to Transfer Data Securely for AI Training
Handing over data is the moment when a well-planned partnership can go wrong if it is rushed. A secure transfer is not just about encryption; it covers what is sent, when, by whom, to whom, and what happens to it afterwards. This guide walks through the steps that make a transfer safe and auditable.
Read guide →How to Prepare Your Company's Data for AI Licensing
Preparing data for an AI licensing deal is less about technical work than about clear decisions: what you have, what you own, what you are willing to include and how it should be protected. Done in the right order, preparation is manageable for a small internal team. This guide sets out a practical sequence you can follow.
Read guide →Key Contract Terms in an AI Data Licensing Agreement
The signed agreement is what actually protects you in a data partnership. Conversations, emails and good intentions matter far less than what the contract says about scope, use, security and responsibility. This guide explains the main terms in plain English so you can read a draft with confidence and know what to ask your lawyer about.
Read guide →What Are the Risks of Selling Company Data to AI Companies?
Licensing or selling operational data for AI training can be a sensible new revenue stream, but it is not risk-free, and anyone who says otherwise is not being straight with you. This guide sets out the main risks honestly, explains how serious each one tends to be, and describes the practical measures that reduce them.
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