dRISK utilizes patented knowledge graph technology to automatically integrate diverse data sources into a unified schema, enabling users to execute complex queries without coding. This approach enhances the safety and efficiency of autonomous vehicles by quickly identifying critical risk patterns from large datasets.
Funding
Funding not disclosed
IUFounders
Product
Problem
Analyzing diverse datasets to identify risk patterns in autonomous vehicle development is complex and time-consuming, often requiring specialized coding skills and data science expertise. Traditional data science methods struggle to efficiently extract meaningful insights from large, heterogeneous databases, hindering the rapid identification of critical safety risks.
Solution
dRISK offers a knowledge graph-based platform that automatically integrates diverse data sources into a unified schema, enabling users to execute complex queries without writing code. The platform allows users to visually interact with data and extract meaningful patterns through spatial queries. By simplifying complex queries into single steps, dRISK helps users quickly identify critical risk patterns from large, heterogeneous datasets, improving the safety and efficiency of autonomous vehicle development. The platform leverages patented knowledge graph technology and AI agents to provide traceable, justifiable insights, empowering lean data science teams to become analytical powerhouses.
Target Audience
The primary users are data-driven decision-makers in the autonomous vehicle industry, as well as those in healthcare, auditing, and risk analysis.
Features
- Automated data integration from diverse sources into a unified knowledge graph
- No-code query execution for simplified data analysis
- Visual-spatial query interface for intuitive data interaction
- AI agents that provide traceable and justifiable insights
- Machine learning tools accessible without coding
- Dynamic schema for handling evolving data structures
- Complete solution for data storage and analytics