Monetizing Data
Safely is No
Longer
Impossible.
Safely is No
Longer
Impossible.
PVML helps you unlock new business opportunities
with third parties and clients, without
compromising data privacy, by combining our
Privacy-Enhancing Technology with AI.
PVML shifts the
paradigm of data
sharing
- Give full access to both sensitive and non-sensitive data.
- The strongest Privacy-Enhancing Technology safeguards your private data.
- Empower your clients to effortlessly extract value from data, without extra effort on your part.
Why is data monetization so challenging?
And how PVML overcomes these obstacles.
Data Privacy
Sharing or monetizing data requires strong privacy
guarantees to ensure trust and compliance.
Our platform allows companies to monetize insights derived from multiple data sources without moving the data, without having to tag PII or mask the data, and without risking customers’ privacy.
Extracting Customized Insights
Sharing or monetizing data requires providing third
parties with convenient ways to extract what is
valuable to them in this data, without imposing
customization efforts on you.
With PVML, third parties can generate visually compelling dashboards from free-text analyses or work hands-on with SQL notebooks. Meanwhile, you gain visibility into their demand and activity.
Our Solution
We got you covered from the privacy to
the insights.
User-Friendly Interface
Free-text data analysis using AI (we also offer SQL-based options for the tech-savvy).
Privacy-Enhancing Technology
We use a game-changing data protection tech to unlock previously out-of-reach data. Discovery Mode allows you to get comprehensive insights and competitive benchmarks by analyzing broader data.
Permissions Enforcement in Chat
Enjoy the power of AI without worrying about
sensitive data
Our Solution
We’ve got you covered, with less configurations and more value.
Our Privacy
Enhancing
Technology:
Differential Privacy
Differential Privacy (DP) is a mathematical framework
that offers the strongest data safeguard in data-
driven systems and is currently employed by tech
giants like Google, Apple, and Microsoft.
DP achieves this by adding controlled noise to the output of a query or algorithm, rendering it statistically indistinguishable whether a specific individual’s data was included or excluded.
Security and Compliance
