1
Personalisation fails without user context.
A model with no user data serves everyone the same experience. That‘s a revenue problem, not a technical one.
Companies need user context.
Users want to share it.
The bottleneck isn't the AI.
It's the infrastructure between your product
and the context your users are willing to give you.
The problem
The context your AI needs exists. Getting user permission to use it is the hard part.
2
The bottleneck is no longer the model.
Foundation models are good enough. The gap is the signal you‘re feeding them about each user.
3
83% of users will share data for a better experience.
The demand side isn‘t the problem. The infrastructure to capture that willingness cleanly is.
4
Building data integrations one by one doesn‘t scale.
Every platform API is different. Every integration breaks. It costs months and stays fragile.
Products
Two products. One consented data infrastructure.
Context Gateway
A user context API that lets users share their cross-platform data with your application. Real user context across health, financial, social, and behavioural data. From the first interaction, with no custom platform integrations.
Built for product teams who need to know their user before they have a purchase history.
Learn moreDatasets
Licensed ground truth datasets for AI model training. Sourced directly from real users across health, financial, behavioural, and communication domains. Consented at the record level. Auditable by your team before you buy.
Built for AI and ML teams who need training data they can defend legally and commercially.
Learn moreFrom the lab
Case studies, research, and press.
Fashion • Primary Source Datasets
Fortune 500 fashion brand builds real-time taste prediction engine.
A Fortune 500 fashion retailer used primary source datasets to infer style preference before a customer had any purchase history.
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Research · October 30, 2025
Open Problems in AI Data Economics
We introduce data economics as a coherent field and define the open problems that haven‘t yet been formalised. Most AI economics research focuses on downstream effects. We argue you can’t understand AI‘s economic trajectory without studying how data, compute, and labour interact at the production layer.
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Experiential • Brand Activation • Context Gateway
Thweet creates 1,497 one-of-one guest experiences.
Thweet turned one creative concept into 1,497 personalised experiences during Korea Blockchain Week 2025.
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Press · MIT News · April 2025
Vana lets users own a piece of the AI models trained on their data
MIT News on the founding story, the Media Lab origins, and the case for user-owned AI.
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AI Training Data • Context Gateway
Frontier AI Lab sources real human conversation data with consent, redaction, and provenance.
A frontier AI lab used Context Gateway to source real human conversational data with documented consent, redaction, and provenance.
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Research · August 1, 2024
Model Influence Functions: Measuring Data Quality
A framework for attributing model outputs to training data contributions. The methodological foundation for pricing and valuing datasets in commercial AI pipelines.
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