How to Sell Your Company's Data for AI Training

Why AI developers want operational data

Frontier AI models have already been trained on most of the text that is publicly available. What they have seen far less of is how work actually gets done inside businesses: how an engineer breaks down a feature request, how a support agent resolves a tricky ticket, how a finance team closes the month. That kind of material lives in private systems, and it is what helps models learn to plan, use tools and complete multi-step tasks.

This is why ordinary business records, the ones your team creates every day without thinking about it, can have value. The most useful data shows a complete piece of work: the objective, the tools used, the decisions made along the way and an outcome that can be checked. A ticket with its full history and resolution is more useful than a ticket title alone. A code repository with its planning documents, issues and reviews is more useful than the final source code alone.

Step 1: Take an honest inventory

Before talking to anyone, list the systems your company has used over the years and roughly what each contains. You do not need to export anything at this stage. The goal is to understand the shape of what you have so that you can describe it in a first conversation.

For each source, note how many years it covers, whether the history is complete or has gaps, and whether it is linked to other systems. Linked records, such as a Jira issue that references a pull request and a Confluence design page, tend to be more useful than isolated files.

• Code and engineering: repositories, commit history, pull requests and reviews, test suites, CI/CD logs, release notes
• Planning and product: PRDs, roadmaps, architecture and design documents, issue trackers
• Operations: SOPs, process documentation, internal wikis, screen recordings of routine tasks
• Customer-facing work: support tickets, chat transcripts, call notes, escalation records
• Business systems: ERP, CRM, accounting records, spreadsheets and financial models

Step 2: Confirm you have the right to share it

Owning a server does not automatically mean you own every piece of data on it. Before you put anything in scope, check three things. First, whether the data or code belongs to you or to a client: agencies and IT service firms often build work that is contractually owned by the customer. Second, whether any agreements, such as customer contracts, vendor terms or partnership agreements, restrict how the data may be used. Third, whether the data contains personal information that is covered by privacy laws such as GDPR, India's Digital Personal Data Protection Act 2023 or HIPAA.

None of these automatically rule out a partnership. Personal information can often be removed or replaced through anonymization, and client-owned material can simply be left out of scope. But you should know where these lines are before you start, and it is sensible to involve your legal adviser early.

Step 3: Start the conversation without sending data

A reputable data partner should never ask you to upload raw data in a first conversation. At Momentra, the first step is simply a contact form: you share your name, company, industry and the kinds of data you might be interested in discussing. Nothing else is needed.

The next step is an initial value assessment. This is a discussion about the type of data you hold, how complete it is, how scarce it is, and what kinds of AI tasks it could support. You describe your data; you do not hand it over. This conversation helps both sides decide whether it is worth going further before anyone spends time on legal paperwork.

Step 4: Set boundaries in an NDA before any sample

If the assessment looks promising, the next step is a non-disclosure agreement that sets the boundaries of the relationship. This should happen before any sample is reviewed. A good NDA for this purpose covers more than secrecy: it defines what the sample may be used for, how long it may be retained, who may access it and what security measures apply.

You decide what is in scope. If a particular repository, customer account or time period is sensitive, leave it out. It is perfectly reasonable to start with a narrow scope and widen it later once trust is established.

Step 5: Review the offer, then de-identify and transfer

Once a sample has been reviewed under the NDA, Momentra prepares a purchase proposal and contract. The agreement governs the scope of data, the permitted uses, how the data will be transferred and how it will be processed. Depending on your goals, the arrangement may be a licence, an outright purchase, or a custom project partnership.

After signing, the data is prepared for transfer. Professional anonymization uses purpose-built tools to detect, remove or replace sensitive information such as names, contact details, credentials and customer identifiers before the data is used further. Transfer then happens through a secure channel agreed in the contract.

Mistakes to avoid

Most problems in data deals come from moving too quickly or too loosely. A little discipline at the start saves a lot of trouble later.

• Sending raw exports before an NDA is signed
• Including client-owned code or documents without checking contracts
• Assuming that deleting names alone makes data anonymous
• Agreeing to vague terms about how the data may be used or how long it is kept
• Overlooking secrets such as API keys and passwords committed to code or pasted into tickets
• Expecting a fixed price list: value depends on the specific data and is assessed case by case