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Data abstraction is the creation of a simplified representation of the information in a database: by representing its essential, aggregated features while hiding unnecessary user-level details, data abstraction protects the privacy and confidentiality of data while preserving a machine’s ability to learn from it. Abstractions are anonymous, aggregated, irreversible representations of data sets. The Fantix app generates them locally in your system or cloud instance so that no user level information ever leaves your servers. Your data is safe because we don’t transmit or transfer it anywhere.

Fantix is not a data cleanroom. We aim to replace data clean rooms in use cases where probabilistic, synthetic data is nearly as good (or better) than real-world data: by removing the risk of transmitting real user-level data, Fantix can make clean rooms un-necessary in many use cases.

No. Fantix is a cloud application and a desktop application that runs on MacOS, Windows and Linux. We do not currently have any apps for iOS and Android.

Fantix does not collect Personally Identifiable Information.

Data aggregation does not offer full protection: there are techniques to reverse the process and extract personal information from aggregated data. An abstraction is not reversible because it is not data: it is a representation. What is so innovative about Fantix is that our application does not process data, it understands it. It understands data and collects it as an abstraction. A single abstraction is meaningless, but it has the power to produce insights when compared with other abstractions.

Synthetic data is artificially generated information that mimics real-world data characteristics. It serves as a privacy-conscious, scalable, and customizable alternative for UX personalization, targeting, and product development without using actual, potentially sensitive, data.

Four reasons: it's 100% privacy-safe because it does not contain PII of any kind; it's accurate because AI is trained on many real world data sets and learns how to debias; it's current because it can be updated frequently; it's scalable, both easier and faster to deploy and not bound by the size of a specific real world data set.

CRM data enrichment is the process of adding, enhancing or refining data in a Customer Relationship Management (CRM) system with additional information from an external data source.

The primary advantages of enriching a data set with synthetic attributes generated by AI are privacy safety and ease of use: synthetic attributes are PII free, and our enrichment model(s) can be queried in real time without PII and without a data clean room.

No. Fantix is an AI platform for marketing data science. We train models that support predictive analytics, market research, audience enrichment, etc.

You can query the model by sending three to seven attributes for each user you want to enrich. To give you a simple example, many e-commerce brands and retailers query the model with just RFM quintiles: for each user, they send just three values (numbers from 1 to 5) indicating which quartile the user is in for Reach, Frequency, and Monetary value. No PII is needed.