Find the failure modes your team would otherwise miss.
An evidence-backed FMEA intelligence platform, grounded in peer-reviewed failure literature, not expert memory.
FMEA is only as good as the team in the room.
The methodology is sound. The execution is broken, by design.
3–6 weeks of expert coordination per analysis
No fresh risk analysis: stale knowledge locks in old gaps
S/O/D scores are entirely subjective, no external evidence
Static document, never updated, knowledge never reused
Incomplete failure modes
FMEA may not comprehensively capture all failure modes. Used as a top-down tool it surfaces only major risks, leaving critical gaps undocumented.
Engineering Failure Analysis · Elsevier, 2011Subjective, unreproducible scoring
Human bias dominates S/O/D scoring in both experts and novices. Different teams produce different RPN values for the same risk, giving a false impression of objectivity.
Engineering Failure Analysis · Elsevier, 2012Knowledge never transfers
Acquiring failure modes is labour-intensive and error-prone due to incomplete data and lack of standard vocabulary. Lessons from one project rarely reach the next.
Arabian-Hoseynabadi et al. · Int. J. Electrical Power & Energy Systems, 2010Evidence-backed FMEA, from the ground up.
Start every analysis from global failure knowledge, not a blank row and a whiteboard.
- →Failure modes sourced from 2,800+ indexed peer-reviewed papers
- →Every suggestion links to its source for full audit traceability
- →Supports AIAG-VDA Action Priority (AP) methodology
- →Accept, edit, or reject. The engineer stays in control
Trent 900 Turbofan Engine
Trent 900: assembled
From blank row to evidence-backed FMEA in minutes.
Search by component or system
Enter a component, operating environment, or system. Risk on Radar searches 2,800+ indexed papers instantly.
Review failure modes with citations
A ranked list of failure modes, causes, effects, and controls, each linked to its source. Fully traceable.
Build your FMEA row by row
Accept, edit, or reject each suggestion. The engineer owns every decision. Export or work directly in the platform.
Common questions.
Risk on Radar indexes peer-reviewed failure literature into a structured database of failure modes, causes, effects, and controls, then surfaces that knowledge inside your FMEA workflow. Search by component or system and get evidence-backed suggestions you can accept, edit, or reject. It's a copilot, not an autopilot.
Most tools (APIS IQ, Relyence, ReliaSoft) focus on structured authoring and standards compliance, but they start from a blank row. Risk on Radar's differentiator is the external intelligence layer: a continuously updated knowledge graph from failure literature that no incumbent currently owns.
No. Every suggestion is reviewed and approved by the engineer. The platform surfaces documented evidence so engineers can make better-informed decisions. Full source traceability is maintained for every row.
The initial focus is mechanical and industrial systems: rotating equipment, fluid systems, structural components, and process plant. Standards support includes AIAG-VDA (Action Priority scoring), ISO 26262, and IEC 61508. Automotive, aerospace, medical, and energy verticals are in scope.
Via structured ingestion of open-access and licensed journal literature using publisher TDM APIs (Crossref, Elsevier, Springer Nature). Each record is normalized into a component → failure mode → cause → effect → control taxonomy with source DOI, confidence scoring, and evidence text spans. Human-in-the-loop validation is applied throughout.
Where we're going.
From Failure Intelligence Engine to cross-domain risk analysis: three phases, one mission.
Failure Intelligence Engine
A living knowledge graph indexed from 2,800+ peer-reviewed failure papers across 75+ mechanical component types. The database that makes evidence-backed FMEA possible.
- Structured ingestion via Crossref, Elsevier TDM, and Springer Nature APIs
- Component → failure mode → cause → effect → control taxonomy
- DOI-linked citations with confidence scoring and evidence text spans
- Human-in-the-loop validation at every ingestion stage
System-Level Risk Analysis
Cross-system failure propagation and dependency mapping. Understand how a single component failure cascades through the full system, before it happens in the field.
- Graph-based failure propagation modelling
- Cross-component dependency visualisation
- Interface failure mode library
- Integration with AIAG-VDA Action Priority scoring
Cross-Domain Failure Intelligence
Unified failure knowledge across automotive, aerospace, industrial, and energy. What failed in a wind turbine bearing might prevent a failure in a jet engine. We connect the dots.
- Multi-domain taxonomy alignment
- Cross-industry failure pattern detection
- Domain-adapted severity and occurrence tables
- Standards mapping: ISO 26262, IEC 61508, DO-178C
Get early access.
Join a small group of reliability and quality engineering teams shaping the product.
- Priority platform access
- Input on the product roadmap
- Founding team pricing
- Dedicated onboarding support
You're on the waitlist.
We'll reach out as soon as early access opens.