Cornerstone AI develops a machine learning platform that automates the cleaning and preparation of real-world healthcare data by generating unique data cleaning rules tailored to each dataset. This technology addresses the inefficiencies of traditional data cleaning methods, enabling organizations to enhance data quality and accelerate analysis, ultimately improving insights from clinical datasets.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Real-world healthcare data is often riddled with errors, inconsistencies, and missing values, requiring data science teams to spend significant time on manual data cleaning and preparation. Traditional rule-based data cleaning methods struggle to keep pace with the increasing volume and complexity of healthcare data, becoming a bottleneck for analysis and insight generation.
Solution
Cornerstone AI offers a machine-learning platform designed to automate the cleaning and preparation of real-world healthcare data. The platform automatically detects data schemas, identifies errors, and augments clinical terminology without requiring manual configuration or fixed rules. By generating unique, clinically relevant data cleaning rules tailored to each dataset, Cornerstone AI enables organizations to improve data quality, accelerate analysis, and unlock deeper insights from clinical datasets. The system provides a full audit trail of all changes and allows for easy data export.
Target Audience
Cornerstone AI targets healthcare organizations, including pharmaceutical companies, medical device companies, and hospitals, that seek to improve the quality and usability of their real-world data for analysis and AI/ML applications.
Features
- Automatic data structure profiling to detect table structure, data types, and field relationships
- AI-driven error detection using machine learning models to identify outliers and flag errors without SQL rules or manual transformations
- Data standardization and augmentation to industry dictionaries (e.g., ICD-10-CM, CPT)
- Missing data imputation and augmentation of medical terminology with hierarchical information
- Automatic structure detection and multi-source harmonization
- Data quality scoring
- HIPAA compliance, audit trail, and on-premise and hosted options