About PVML
PVML is an AI-powered enterprise data access and analytics platform that helps organizations safely use their internal data with AI tools without moving or duplicating it. It creates a secure “virtual database layer” so companies can analyze sensitive data using natural language or AI models while keeping strong privacy and governance controls. The main focus is enabling safe AI usage on enterprise data with built-in security and compliance.
Feature Highlights
PVML allows companies to connect multiple data sources and query them in real time without physically moving or copying the data, which reduces security risks and storage overhead.
It uses differential privacy and advanced security layers to protect sensitive information while still allowing meaningful data analysis.
The platform supports natural language queries, so users can ask questions in plain English and get insights from complex enterprise data.
It provides a virtual database system that sits on top of existing infrastructure and applies access control and governance rules before data is returned.
PVML integrates with AI tools and LLMs, allowing organizations to safely connect data to systems like ChatGPT-style applications for analysis.
It includes monitoring, auditing, and permission tracking to ensure enterprise-grade compliance and visibility.
Use Cases
Enabling companies to safely use AI chat systems on internal business data without exposing sensitive information
Allowing data analysts to query large enterprise databases using simple natural language
Helping organizations unify multiple data sources into one secure analytics layer
Supporting business intelligence dashboards with real-time secure data access
Reducing data duplication and infrastructure costs in large companies
Improving decision-making by giving faster access to enterprise insights
Building secure AI agents that can work on private company data without violating compliance rules
Creating privacy-safe data monetization systems for sharing insights with third parties
Running experimental AI workflows that combine LLMs with protected enterprise databases