Partridge Systems develops the Databahn platform, which provides a validation toolchain for automotive AI/ML systems, enabling efficient processing and collaboration of petabyte-scale real-world data. This platform addresses the challenges of ensuring safety and performance compliance in increasingly complex industrial systems by significantly reducing validation cycle times and improving cost efficiency.
Funding
$3.1M 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
Validating AI/ML systems for automotive and smart factory applications requires processing and managing exponentially growing volumes of real-world data. Ensuring the safety and performance of these systems is challenging due to the complexity of data movement, processing, and collaboration, leading to long validation cycles and high costs.
Solution
Partridge Systems' Databahn platform provides a comprehensive validation toolchain for industrial AI/ML systems, addressing the challenges of data processing and collaboration at petabyte scale. The platform integrates with existing data infrastructure to streamline data movement, enhance data discoverability, and improve data quality. Databahn offers advanced MLOps and DataOps capabilities, along with data virtualization technology, ensuring data accessibility across the organization. By optimizing the validation process, Databahn significantly reduces validation cycle times and improves cost efficiency.
Target Audience
The primary customers are engineers and organizations involved in developing and validating AI-powered industrial systems, including automotive ADAS/AD and smart factory applications.
Features
- Data virtualization technology for data discoverability and accessibility
- High-performance custom data loggers and secure storage systems for data collection campaigns
- Standardized cloud-native edge cloud and container technology
- Flexible data pipelines and data ingestion tools for data cleaning and structuring
- Data analytics engine and collaboration tools for data standardization across teams
- Monitoring, maintenance, and in-person support for reliable operations