We build real and hybrid datasets for AI/ML training on top of our 400+ synthetic data products. To do that, we license and buy real-world data from the organizations that hold it — every record de-identified before it's ever used. If your business is sitting on proprietary data, let's talk about turning it into a revenue stream.
The AI industry is running short on high-quality, domain-specific training data. We started with a synthetic data factory — hundreds of products across our verticals — but the frontier is hybrid: real records anchoring synthetic scale. That real anchor has to come from somewhere. If you hold proprietary, well-structured data, we want to license it. You keep the asset. We handle de-identification and compliance. You earn on data that's otherwise just sitting on a server.
Monetize data you already have. We license or buy it outright, with terms that fit whether you want a one-time sale or ongoing royalties.
Every record is stripped of direct and indirect identifiers before it's used for training. Aligned to HIPAA Safe Harbor, GDPR, and CCPA expectations.
You decide which fields, which segments, and which use cases are in scope. Nothing leaves your definition of acceptable use.
Tabular, text, audio, images, sensor logs, event streams — structured or messy. We handle cleaning, mapping, and validation.
Browse the data types we're seeking, or search by keyword. Don't see yours? We're interested in almost any proprietary, domain-specific dataset — email us and describe it. Every category below is licensed only after de-identification, and can be delivered as a real dataset or blended into a hybrid real-plus-synthetic set.
No categories match that search. Email us anyway — if it's proprietary and well-structured, we're probably interested.
Most AI teams want the authenticity of real data with the scale and edge-case coverage of synthetic. We blend the two into hybrid datasets, using your de-identified records as the statistical anchor. Typical mixes we build, depending on how much fidelity the use case demands:
A light real anchor keeps a large synthetic set honest. Best when the goal is volume, class balance, and rare-event coverage.
Enough real data to capture genuine correlations and messiness, with synthetic filling out the distribution. A common default.
For domains with subtle dynamics a generator can't fully model, or downstream models that are highly sensitive to realism.
The right ratio is ultimately set by an ablation on the target task — we validate empirically and stop adding real data once the metric that matters stops improving. Your data is compensated the same either way: as a standalone real dataset, or as the anchor inside a hybrid one.
Direct identifiers are removed and indirect identifiers generalized or suppressed before data is used for any training or blending — aligned to recognized frameworks such as HIPAA Safe Harbor.
We assess and reduce re-identification risk — including k-anonymity-style checks on quasi-identifiers — before a dataset is approved for use.
Licensing terms fix exactly what the data can and can't be used for. You define permitted uses, exclusions, retention, and territory.
One-time purchase or ongoing licensing/royalty, based on the volume, rarity, and quality of the data. You choose the structure that fits.
Email pradeep@xpertsystems.ai with your vertical, the type of data, rough volume, and format. A short description is enough to start — no data changes hands yet.
Under a mutual NDA we review a small sample, confirm the data is de-identifiable to our standard, and agree the fields and use cases in scope.
Nothing enters training until this is agreedWe de-identify, run re-identification checks, and finalize a licensing or purchase agreement with clear permitted-use terms and your compensation.
Your de-identified data becomes a standalone real dataset or the anchor in a hybrid real-plus-synthetic set for AI/ML training — and you get paid.
Tell us your vertical, the type of data, and the rough volume. We'll come back with whether it's a fit, how we'd de-identify it, and what compensation could look like — as a real dataset or a hybrid anchor.
Email us about your dataPlease include your vertical, the type of data, approximate volume, and format. All data is de-identified before any training use, and licensed under terms you help define. This page is an invitation to discuss, not an offer of terms; nothing here is legal advice — confirm your own rights to license the data before sharing it.