How to source domain data for a vertical AI product

To source domain data for a vertical AI product, define the specific tasks your model must perform, list who holds the records those tasks produce, approach customers, partners and data holders in that order of closeness, and test small samples before committing. Rights and fit matter more than volume.

Why does domain data matter more in vertical AI?

Vertical AI products compete on knowing a field better than a general model does, and that knowledge lives in the records of the people who work in it. General models are trained on public text, but most professional work leaves its traces in private systems.

Consider what is rarely public: how a claims adjuster annotates a file, how a dispatcher words an exception, how an estimator revises a bid. These records encode vocabulary, sequence and judgement that a general model approximates but does not reproduce.

That is why the data question often defines the business. If your competitors can buy the same data you use, your edge is thin. If you hold or can access records they cannot, the product improves in ways that are hard to copy.

What data do you actually need?

Start from the tasks, not from the data. Write down each job the product must do, then name the inputs and outputs that job involves, and ask what records would show those inputs turning into those outputs.

1. List the five to ten core tasks, such as "extract line items from an invoice" or "draft a reply to a delayed shipment inquiry."
2. For each, describe the input (documents, messages, structured fields) and the output a good professional would produce.
3. Identify the records that pair them, such as completed cases, resolved tickets or approved submissions.
4. Note the variation you need: sub-types, regions, languages, document formats, edge cases.
5. Estimate rough volumes for a pilot, not a final target.
6. Decide what you need for training, and separately what you need for evaluation.

The last point is easy to miss. Evaluation data should be drawn from real work and kept apart from training data, so you can tell whether the product works. Our article on evaluating agents with domain benchmarks explains how to use it.

Who holds the data you need?

Domain data is held by the organisations that do the work, and those are also your potential customers, partners and competitors. Mapping holders in advance tells you which approaches are open to you.

Typical holders include:

• Operating companies, who generate the records as part of their business.
• Software vendors serving the industry, who hold records on behalf of customers, often with contractual limits.
• Industry bodies and associations, who may hold shared or standardised material.
• Service providers such as consultants and agencies, who hold documents from many clients, usually under confidentiality.
• Public bodies, who publish some records, with terms that differ by place.

Note who actually controls the rights. A software vendor may host a customer's data but not be free to license it. In that case the customer is the party to approach, or both must agree.

How do you get data from your own customers and partners?

Often the most defensible source is the one nearest you: your customers' work, shared under clear terms and used to improve their own experience. Design this into your product agreements from the start.

Pilot customers often agree to share data in exchange for a better product, a discount or co-development. Make the arrangement explicit in the contract: what data, which uses, whether it can train a model used by others, what happens at termination, and how personal data is handled. Customers' legal teams will ask about all of these.

Do not rely on a vague clause added later. If your terms did not clearly cover training use when the data was collected, you may not be able to apply it retroactively. This is general information and not legal advice; ask a qualified lawyer to draft the data terms of customer agreements.

How do you license data from a company that is not your customer?

Approach holders with a specific, bounded request and a clear explanation of what they get. Companies hold data they have not thought of as an asset, and will respond when the ask is concrete.

A good approach states the task you are building for, the type and volume of data, how you will protect it, what rights you want and what you offer in return. Offer options: a license for a defined use, a revenue share, or access to the finished product. Keep the first conversation small, such as a sample under NDA, as described in how to request a data sample under NDA.

On LogitAtlas, companies list data they already hold, and you can message them, bid, and negotiate a license, with payment held in escrow until you confirm delivery. If what you need is not listed, you can post a data request describing it so holders can respond. Sellers keep ownership and set license terms, so the license remains the controlling document.

Mapping holders in practice

Hypothetical example: a team building software for construction submittals lists contractors, architects, suppliers and the software vendors they all use. They note that contractors hold the submittal histories, vendors hold platform logs but not the rights, and architects hold review comments under client confidentiality. The map shows that contractors are the natural licensors and architects are a later, harder conversation.

Should you buy data, build it or use both?

Most vertical AI teams use a mix: bought or licensed real records for realism, created data for gaps, and synthetic data for coverage. The mix depends on how well the task is documented in existing records.

Buy or license when real records exist and rights can be cleared. Create data when the task has never been recorded, for instance when experts must write the ideal answers. Generate synthetic data to extend rare cases, as long as you keep a real evaluation set. The trade-offs are in synthetic data vs real company data.

The decision also changes over time. Early on, a small licensed set plus expert-written examples may be enough to prove the product. Later, as customers ask for more regions or document types, you will see exactly which holders to approach next, because your evaluation results show where the model is weakest.

Compare total cost, not just price. A cheap dataset that needs months of cleaning and legal review may cost more than a pricier one that is ready to use.

Sequencing the work

Teams often try everything at once and then drown in contracts. A more manageable sequence is to sign one design-partner customer, assemble a first evaluation set from their work, license one complementary external dataset, and only then widen the search. Each step produces evidence that makes the next conversation easier. Holders respond better to a founder who can show an evaluation set and a tested pilot than to one with only a pitch.

How do you check quality and rights before committing?

Test samples against your tasks, review provenance and agree the license before you scale. Doing these in order prevents expensive surprises.

• Run a sample through a small version of each core task.
• Check for duplicates, label errors, missing outcomes and stale records.
• Ask how personal data was handled, and verify the sample.
• Confirm the seller can grant training rights, including rights to keep the trained model.
• Check whether use is limited by field, geography or customer type.
• Agree what happens to the data and the model when the license ends.

Hypothetical example: a startup building a tool for freight exception handling collects a sample of exception notes. The notes are plentiful, but most lack the resolution, so the startup can learn how exceptions are described but not how they are fixed. It asks the holder for the linked resolution records, and the answer determines whether the deal is worth making.

How do you build a data advantage over time?

Design the product so that usage generates better data, under clear permission, and so your evaluation set grows with it. A one-time purchase helps once, while a feedback loop keeps helping.

Capture outcomes, corrections and expert edits made by users. These are exactly the signals that raw archives lack. Ask users' permission clearly, offer opt-outs, and separate training use from the customer's private data where contracts require it.

Meanwhile, keep a register of every dataset: source, license, version, and which models used it. The register is part of your credibility with enterprise customers, who increasingly ask where a model's knowledge came from.

When is it a bad idea to chase more data?

It is a bad idea when your problem is the task definition, the evaluation or the product design, not a lack of data. More data will not fix a vague goal.

Warning signs include having no test set, no clear definition of a correct output, or a model that fails on cases the data already covers. Another is pursuing data you cannot use safely, such as records full of personal information with no lawful basis for your purpose. In those cases, narrow the scope, fix the evaluation, or choose a different source.

For a wider survey of options, see where to buy AI training data.

Frequently asked questions

How much domain data does a vertical AI product need?

It depends on the task, the base model and the method. Fine-tuning for a narrow job can work with thousands of well-chosen examples, while broader capability needs more. Start with a pilot, measure against a real evaluation set, and add data where the results show gaps.

Can I use my customers' data to train my model?

Only if your agreements and applicable law allow it. Contracts should state the data, the purposes, and whether the model benefits other customers. This is general information, not legal advice. Have a qualified lawyer review your terms before you rely on customer data.

Who should I approach first for domain data?

Start with the parties closest to you: pilot customers and partners, who have a stake in the product's success. Then approach other holders with specific requests, and consider marketplaces and data requests to reach holders you cannot identify directly.

Is proprietary data really a moat?

It can be, if it is hard for others to obtain, relevant to the task and kept current. It is weaker if competitors can license the same data, or if the data matters less than model quality or workflow design. Test what actually drives your results.

What if the data I need does not exist?

Create it. Have domain experts produce examples and ideal outputs, or design the product so that users generate and correct them as they work. Supplement with synthetic data for coverage, and keep a small real evaluation set from the start.

If you are ready to look for records, browse the datasets or describe your need and let holders respond.