Description
Lantern is a comprehensive data platform designed for private markets, focusing on validating data at the source to eliminate manual verification processes. It addresses the common industry issue where data trust is built manually at every handoff, from portfolio companies to administrators and board reports. Lantern operates alongside existing systems, continuously running in the background to extract, validate, and deliver data across General Partners (GPs), Limited Partners (LPs), and Fund Administrators.
The platform is built by industry veterans from companies like eFront, Allvue, iLevel, Altvia, and Aztec Group, who have firsthand experience with the challenges of data verification in private markets. Lantern's approach involves three key stages: collecting data from all systems, verifying it through continuous checks, and delivering trusted data for reporting and analysis. This process ensures that every data point is traceable to its source, with 2 million daily tests validating each number.
Lantern's platform is non-disruptive, designed to work alongside existing tools without altering workflows. It provides a validation layer that enhances data accuracy, confidence, and control. The platform supports various reporting tools and systems, ensuring that trusted numbers are available wherever needed, whether in Excel, Snowflake, or through APIs.
The platform's value proposition lies in its ability to provide continuous assurance, where data is automatically validated and verified across all systems. This eliminates the need for manual trust-building at the end of reporting cycles, allowing teams to focus on using data rather than checking it. Lantern is particularly beneficial for firms managing large assets, such as the €40bn Global Private Equity Manager and the €2bn growth equity firm, by streamlining their data operations and enhancing reporting accuracy.
Lantern AI Data Platform's Core Features
Continuous data validation
Non-disruptive integration
2 million daily data tests
100% data traceability
Supports multiple reporting tools
Works alongside existing systems
Extracts and structures data
Enhances data accuracy and control
Provides a validation layer
Automates data assurance
Logs every change and check
Delivers trusted data for reporting
Validates data across GPs, LPs, and Fund Admins
Built by industry veterans
Supports Excel, Snowflake, API, SFTP, and MCP
How to use Lantern AI Data Platform?
Configure: Set up Lantern alongside existing systems
Extract: Pull data from all sources
Validate: Run continuous checks on data
Deliver: Make trusted data available for reporting
Lantern AI Data Platform's Use Cases
- Data Validation
- Reporting Confidence
- Workflow Integration
- Asset Management
- Continuous Assurance
