Cybersecurity
and Compliance
Real-Time Intelligent Diagnostics for Instant Recovery
Your incident response team spends hours manually correlating traces, switching between dashboards, and guessing at root causes. Our AI-powered platform ends that chaos. By integrating Graph Neural Networks with OpenTelemetry traces, our solution delivers real-time root cause identification that compresses MTTR work into seconds of intelligent analysis-automatically detecting where failures originate, why they occurred, and what to fix.
Instead of manual trace correlation and log diving, our platform builds complete execution paths across your entire system in real-time. We analyze service interactions, timing dependencies, infrastructure metrics, application behavior, and data flow to reveal the exact source of every failure with confidence scoring. Topology-aware anomaly detection identifies problems simultaneously across infrastructure, application, and business domains.
Graph Neural Networks reveal the complete causal chain. We cross-correlate data points to visualize exactly how failures propagate through your architecture layer by layer. With automatic root cause ranking, your team receives actionable diagnosis faster than humans could even gather initial information.
Performance regressions that would normally take hours to identify appear in seconds. Silent failures in event-driven workflows, the data loss and corruption in asynchronous systems are instantly diagnosed with complete visibility into how they originated and cascaded through your system. Every root cause comes with visualization showing the exact propagation path and comprehensive proof of causation, so your team knows exactly what to fix and why.
Real-Time Root Cause Identification
Pinpoint root causes without manual work. AI-powered analysis that correlates traces automatically and ranks suspects by likelihood.
Identify the complete causal chains connecting timing dependencies, service relationships, and data flows in seconds.
Compress incident resolution from hours to minutes by receiving precise diagnostics with actionable recommendations.
Detects performance regressions in real-time before customers experience impact, enabling instant validation and rollback.
See the complete propagation story by visualizations showing exactly how failures cascade through your system from origin to impact.
Through real-time root cause identification, DevOps and SRE teams gain immediate diagnostic power:
Robinhood’s Trading Outage (Mar 2020)
What happened:
During a period of extreme market volatility, Robinhood’s trading platform went down for multiple days, preventing users from executing trades during record market gains. The failure stemmed from infrastructure overwhelmed by an unprecedented surge in traffic, exposing scalability limits in their microservices architecture.
Impact:
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Millions of users were locked out during critical trading windows.
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Customer frustration, lawsuits, and regulatory scrutiny followed.
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Lack of real-time visibility delayed fault isolation and prolonged downtime.
How Real-Time Root Cause Identification Could Help:
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OpenTelemetry traces could have revealed the exact service where failures originated, mapping the full lifecycle of user transactions.
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AI-driven root cause ranking would have surfaced the overloaded queuing or database layer as the primary issue.
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Dynamic dependency mapping could have exposed cascading impacts across services tied to trade execution and account updates.
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With intelligent observability, engineers could have restored operations within minutes instead of hours—preserving user trust and market credibility.
Google Cloud IAM / API Outage (Jun 2025)
What happened:
A global outage disrupted Google Cloud services, impacting major platforms like Spotify, Discord, and Shopify. The incident stemmed from an invalid automated quota policy update in the IAM and API management layer, which propagated globally due to insufficient validation and error handling.
Impact:
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Authentication and quota-dependent systems across multiple organizations went down simultaneously.
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Engineers struggled to pinpoint whether IAM, the API gateway, or quota enforcement was the root cause.
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Customers experienced prolonged service disruptions, leading to reputational damage and operational losses.
How Real-Time Root Cause Identification Could Help:
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OpenTelemetry traces could have visualized API and IAM request flows, pinpointing failures originating in the quota enforcement layer.
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AI-driven root cause ranking would have identified the invalid policy change as the primary fault instead of chasing secondary symptoms.
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Dynamic dependency mapping could have exposed all systems reliant on IAM and quota policies—enabling faster isolation, rollback, and recovery.
Real world examples where Beemon could make a difference:
Marketing department
Headquarters