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A complete toolkit for automatically pinpointing your system's weak points across 2,000+ scenarios — without ever touching production.
The 10 core capabilities behind zero-risk diagnosis
Network latency, process failures, resource exhaustion, cascading dependency outages, latency, blast radius, SLA validation, and more — powered by Monte Carlo methods, Markov chains, and queueing theory.
From a single-node failure to cascading multi-region outages. Auto-generated from your system's YAML topology — a 10-component setup produces 2,000+ unique scenarios.
The N-Layer Model is an analytical framework that decomposes your availability ceiling into 5 independent constraint layers. It pinpoints which layer is the bottleneck and shows exactly where to invest.
Uses Anthropic's large language model, Claude, to automatically identify root causes from simulation results and generate improvement proposals — ranked by impact and remediation cost — in language both engineers and executives understand.
A research-prototype mapping to the Digital Operational Resilience Act (EU financial regulation). Generates evidence drafts with audit trails and risk assessments — but it is not an audit certification and cannot prove regulatory compliance without independent legal and technical review.
Automatically ingests the CVE database and NVD feeds, then simulates how known vulnerabilities propagate into cascading system failures.
Integrates live production performance metrics, trace correlation, and anomaly detection with your simulation results. Ships with a dashboard of 35+ monitoring views.
A research-prototype mapping to Japan METI's AI guidelines and ISO 42001 requirements. Generates evidence drafts as input for AI-governance review — but it is not an audit certification and requires independent legal review.
Auto-generates runbooks, recovery scripts, and Terraform patches from simulation results — cutting incident response time from hours to minutes.
A topology editor, scenario explorer, 5-layer drill-down, heatmaps, DORA research drafts, and executive summaries — all in one web dashboard.
Simulate how infrastructure failures cascade into AI-agent misfires — before they ever happen in production.
Tracks how infrastructure failures (database down, cache outage) cascade into wrong answers from AI agents. Detect 'silent degradation' — output that looks fine but is wrong — before it ships.
The three pillars of AI-agent resilience: simulate failure scenarios, assess release risk via blast-radius analysis, and auto-generate monitoring rules.
Bring AI agents, LLM endpoints, tool services, and agent orchestrators into the same dependency graph you already use for conventional infrastructure.
Covers wrong answers, context overflow, rate limiting, token exhaustion, tool failures, infinite loops, prompt injection, confidence drift, reasoning collapse, and error amplification.
Experience all of FaultRay's core features on the free plan. No credit card required.