Risk on Radar is a reliability intelligence platform that transforms fragmented engineering failure evidence from 2,800+ peer-reviewed papers into structured, searchable knowledge for FMEA, root-cause analysis, and system-level risk assessment. The platform enables cross-domain failure pattern transfer, allowing insights from one engineering system to inform risk analysis in another. It provides evidence-backed failure modes with full citations, supporting reproducible and defensible reliability decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional FMEA and reliability analysis rely on subjective S/O/D scoring, static failure libraries, and siloed knowledge scattered across papers, reports, and team expertise. This leads to unreproducible risk assessments, outdated information, and an inability to transfer failure patterns across different operating contexts or engineering domains.
Solution
Risk on Radar turns fragmented engineering failure evidence into adaptive reliability intelligence by continuously structuring peer-reviewed literature into reusable knowledge. The platform indexes 2,800+ papers, extracting failure modes, causes, effects, controls, and operating context into a living knowledge engine. It supports system-level risk analysis by reasoning over subsystem dependencies and similarity matching, enabling cross-domain transfer of failure patterns—such as bearing fatigue under cyclic load—from source domains like wind turbines to target contexts like turbofan gearboxes. Engineers can search by component or system to instantly retrieve evidence-backed failure modes with full citations, supporting FMEA, RCA, predictive maintenance, and operational risk assessment with traceable, defensible data.
Target Audience
Primary users are reliability engineers, FMEA practitioners, and asset owners in industries such as aerospace, energy, manufacturing, and transportation who need evidence-based, defensible risk assessments for complex engineering systems.
Features
- Living Failure Knowledge Engine that continuously structures failure evidence from 2,800+ peer-reviewed papers into reusable reliability knowledge
- System-level risk analysis with subsystem dependency reasoning and similarity matching
- Cross-domain failure intelligence that transfers failure patterns across operating contexts and engineering domains
- Evidence-backed FMEA with AIAG-VDA AP readiness and full citations attached to every failure mode
- Searchable database by component, operating environment, or system with instant retrieval of indexed papers
- Radial stack visualization showing failure mode evidence distribution across system components (e.g., turbofan engine sectors)
- Operating context parameters including load spectrum, temperature, lubrication regime, duty cycle, environment, and maintenance history for context-aware risk assessment