With AI is Now
With AI is Now
PVML helps you unlock access to sensitive data
using free text, without compromising data privacy,
by combining our Privacy-Enhancing Technology with AI.
PVML shifts the
paradigm of data
- Empower users to effortlessly extract value from data using simple text.
- Give easy access to both sensitive and non-sensitive data.
- The strongest Privacy-Enhancing Technology safeguards your private data.
No grey area
Enjoy the power of AI without worrying about sensitive data leaking. PVML Automates privacy at the output level, enabling the analysis of even the most sensitive data without overthinking.
Why is incorporating AI with sensitive data so difficult?
And how PVML overcomes these obstacles.
Data Privacy and Permissions
Unlocking access with AI requires strong privacy
guarantees to ensure trust and compliance.
Our platform enforces permissions when data is accessed with AI, without the need to tag Personally Identifiable Information (PII) or mask the data, and without risking customers’ privacy.
Live Data Analysis
Language models can be fine-tuned on large databases, but besides being a long and costly process, it still doesn’t allow you to analyze live data.
PVML uses large language models to understand requests in natural language, without training on your data, thus allowing you to analyze constantly-evolving data sources in real time.
Language Model Hallucinations
Long, detailed outputs can seem really convincing,
but we all know that language models can fabricate things.
With PVML, you don’t have to manually fact-check the outputs every time. The language model generates a recipe that we turn into a query the database can accept. This means you only get results based on your data.
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.