Synthetic data has a quality ceiling.
Models learn from outputs of other models. The ceiling compounds. In health, finance, and behavioural modelling, the gap between synthetic and real becomes critical.
DatasetsData sourced directly from
Data sourced directly from
real human experience.
One contributor. All linked. Ground truth at a scale no lab can produce.
For bespoke data collections, domain exclusivity, or volume licensing: talk to us
Why synthetic data, annotation, and scraping all fall short.
Annotation is not observation.
Contractors following prompts don’t behave the way real users do. The signal is structured but artificial. That limitation is baked into every model trained on it.
Scraped data is a growing legal liability.
EU AI Act, ongoing litigation against major labs, evolving platform terms. The legal and reputational exposure from unattributed training data is no longer theoretical.
The data you actually need is ground truth.
Observed behaviour, sourced directly from real human experience. How people communicate, spend, move, and make decisions in real life. Not reconstructed from prompts. Not inferred from model outputs.
What we have
Every signal linked to a single contributor.
Primary Source Datasets connect health, financial, communication, and behavioural signals to the same contributor, with record-level consent and provenance throughout.
Health & Wellness
Biometrics, sleep, activity, and nutrition linked across shared devices and apps.
Financial Behaviour
Spending patterns, transactions, and saving behaviour drawn from real financial lives.
Communication & Language
Real text and speech, with shorthand, register, and code-switching intact.
Behavioural & Preference
App usage, browsing, listening, social behaviour, and location linked together.
In practice
What ground truth data makes possible.
Fashion · Retail AI
Fortune 500 fashion brand: taste prediction without a purchase history
Spotify listening history and social data used to build an individual style preference model. The personalisation engine knows a new customer’s aesthetic before they’ve clicked a single product. No survey. No onboarding form. No historical transaction data required.
→ Read case studyLongevity · Health AI
Avinasi Labs: longevity prediction from real human biomarkers
Clinical datasets are too narrow. Aggregate health data lacks the cross-domain richness needed to model biological ageing. Ground truth biomarker data linked across domains, sourced directly from contributors, gave Avinasi Labs the signal their model needed at a scale no clinical dataset could supply.
→ Read case studyWhat you get with every dataset
Every dataset comes with the documentation to defend it.
- ✓Full consent chain documentation. Record-level traceability to a specific permission event
- ✓Published collection methodology. Available for review before purchase
- ✓Benchmarking data. Measure what you’re buying against your existing datasets
- ✓Domain and exclusivity-based licensing. Structured for enterprise procurement
Get access
Browse OTS datasets. Commission a bespoke collection. Either way, you evaluate before you commit.
The dataset catalog gives you immediate access to training-ready data across health, financial, behavioural, and conversational domains. Browse structure, coverage, and sample records before any commercial conversation. Every dataset ships with a data card: methodology, collection conditions, consent chain, and benchmark comparisons.
Off-the-shelf
Training-ready OTS datasets available for immediate licensing. Browse by domain, data type, and coverage. Request a sample data card to evaluate fit before purchase.
Browse the dataset catalogBespoke
Domain-specific requirements, longitudinal collection, exclusivity arrangements, and volume licensing. We scope and source to spec. Talk to us before you write the brief.
Request a scoping call