Delivers Visibility Across 28B+ Monthly Transactions
80% reduction in MTTD/MTTR
28B+ transactions monitored monthly
5x faster incident detection
10x faster RCA
How VuNet AI Works
vuCoreML - The engine behind the insights
Built on a contextual data lake enriched with domain knowledge, our vuCoreML engine understands heterogeneous data sources, banking workflows, payment journeys, and regulatory requirements like RBI’s FREE-AI.
The Data Layer
- Brings together all enterprise data using domain-centric adaptors
- Works consistently across diverse datasets and customer patterns.
- Connects IT events to business outcomes. It knows that a high-latency signal isn't just a server issue; it’s a potential UPI transaction failure.
The Model Layer
- Detects subtle anomalies and change point deflections without static thresholds using domain trained anomaly and ensemble models.
- Flink routing selects the best-fit model per metric, while correlation, RCA, forecasting, RAG / LLMs and domain SLMs drives noise reduction, root cause insight with specific natural language reasoning and prediction.
The Action Layer
- Delivers the ORR Advantage (Observations → Recommendations → Resolutions), turning insights into guided or automated self-healing actions.
- Reduces manual intervention while improving speed and consistency of recovery.
Our ORR Model
From Data to Action
Detect issues, get AI-powered recommendations, and automate resolution - continuously learning from every outcome to improve observability and response.
Features
The power of vuCoreML
From anomaly detection to automated root cause analysis and self-healing workflows, vuCoreML delivers AI-powered capabilities across your entire observability stack.
Observability
Business-Context Alerts: Automated slice-and-dice alerting with business, geo, and time context turns a flood of logs into relevant signals.
Noise Reduction: Automatically correlates alerts based on historical co-occurrence and semantic similarity, reducing alert fatigue and operator effort.
Unified View: Group related alerts across infrastructure, application, and journey layers into single actionable insight, prioritizing critical issues.
Context-Aware Anomaly Detection: Automatically spot unusual patterns across metrics, logs, and traces. Our flink-based and lightweight anomaly detection techniques learn normal behavior to alert only on genuine deviations, reducing noise.
Recommendability
RCABot: Cut through the noise with an AI-driven engine that pinpoints root causes across distributed architectures.
Topology-Aware Correlation: Correlates alerts by monitoring "golden signals" of components involved in specific business journeys, highlighting the real fix with confidence scores and flame graphs.
Actionable Recommendations: Moves beyond observation to provide specific recommendations, such as optimizing resource allocation or investigating specific process configurations.
Experience & Performance Forecasting: Leverage ML-driven scores like User Experience Index (UEI) to anticipate performance degradation and understand its impact on customer journeys.
Proactive Issue Identification: Forecast capacity needs and detect signal deviations (like server metrics or transaction volume) to prevent problems before they impact the business.
Resolutions
Automated Runbook Execution: Execute predefined remediation steps for common scenarios like capacity scaling, clearing queues, or restarting failed processes.
Self-Healing Workflows: Trigger automated resolution actions based on AI recommendations; from restarting services to scaling resources or rerouting traffic.
Orchestrated Incident Response: Coordinates actions across systems and teams
Ticketing Channels Integration: Automatically create tickets in ServiceNow, Jira, or PagerDuty with complete incident context. Close the loop by updating tickets when issues are resolved.
Multi-Channel Notifications: Alert teams via WhatsApp, Teams, Slack, email, or SMS with contextual information and resolution status updates.
Ved AI
Ved is VuNet’s GenAI assistant for observability.
Conversational Insights: Allows teams to interact with a unified knowledge base of observability data, VuNet platform docs and enterprise data using natural language instead of writing queries
Self-Service Troubleshooting: Unlock faster insights, perform ad-hoc analysis, and troubleshoot issues without manual dependencies or complex query languages.
Built to reason about complex systems, not just collect signals
Domain-aware intelligence that aligns technical behavior with business outcomes
Explainable insights that drives confident decisions
Proactive and automated by design and not reactive or manual
Built for high-volume transaction-intensive environments for precise insights
Designed for scale, change, and continuous evolution
Performs consistently across diverse datasets and customer patterns