NeuBird AI

What NeuBird AI does

NeuBird AI is a production operations agent that detects, investigates, and resolves production incidents autonomously across the entire stack, 24/7, before they impact customers. It applies advanced context engineering and deep domain expertise to correlate telemetry, surface risks early, and automate root cause analysis securely across hybrid cloud environments. Built for enterprise SRE, platform, DevOps, and on-call teams, NeuBird AI aims to prevent issues before impact, resolve incidents in minutes, and operate production systems autonomously. It integrates with existing observability, cloud, DevOps, and ITSM tools via API, correlating signals across them to deliver autonomous root cause analysis. The platform is designed with a multi-layered architecture for reliability, security, and extensibility, ensuring data stays within the customer's VPC and sensitive information never reaches LLMs directly.

Products

• AI SRE AgentMoves from alerts and guesswork to autonomous incident investigation powered by AI-driven context engineering. It detects, investigates, explains, and remediates without prompting, aiming for significant MTTR reduction, automated root cause analysis, hypothesis-driven investigation, GitHub-integrated fix suggestions, and preventive risk detection.
• The Production Operations AgentProvides autonomous incident intelligence for production environments. Instead of reacting to alerts, it continuously analyzes telemetry, investigates issues, and delivers clear, evidence-based answers to resolve faster and prevent future incidents. Key features include real-time investigation workflows, intelligent triage and routing, cross-tool telemetry correlation, and alert noise reduction.
• Agentic ObservabilityOffers AI-driven alerting that prevents alerts by predicting risk, suppressing noise, and taking autonomous preventive action to fix conditions that generate alerts. It focuses on predictive risk detection, autonomous preventive actions, AI-driven alert noise suppression, and closed-loop alert tuning.
• Platform for Incident ResponseAn incident response platform that routes, investigates, and closes alerts automatically, without the need for a war room. It includes on-call schedules, escalation policies, and autonomous resolution capabilities.
• Agentic Context EngineA context engineering platform for all production operations workflows. It continuously enriches context with every investigation, enabling AI agents to reason about the entire infrastructure. It features causal reasoning, dynamic context, sandboxed execution, domain skills, alert intelligence, and preventative analysis.

Key capabilities

• Autonomous incident investigation
• Automated root cause analysis
• Real-time production context
• Autonomous incident resolution
• Preventive risk detection
• Runbook automation
• Alert triage and noise reduction
• Cost optimization
• Cross-tool telemetry correlation
• Hypothesis-driven investigation
• GitHub-integrated fix suggestions
• Predictive risk detection
• Autonomous preventive actions
• AI-driven alert noise suppression
• Closed-loop alert tuning
• Intelligent alert routing and on-call scheduling
• Auto-close incidents via autonomous investigation
• Evidence-based escalation suppression
• Anomaly and degradation analysis
• Early signal correlation
• Operational efficiency insights
• ML-powered detection of unusual patterns
• Automatically group related alerts to reduce noise by 90%
• Predictive Alerts
• Trace issues across services to pinpoint the source
• Impact Assessment
• Context Aggregation
• Execute existing runbooks with full audit trails
• Safe Remediation with guardrails
• Human-in-the-Loop for critical actions
• Multi-layered architecture for reliability, security, and extensibility
• Data Layer for metrics, logs, traces, events & alerts
• Connection Layer for cloud APIs, MCP, observability APIs, ITSM & chat connectors
• Intelligence Layer for context engine, skills hub, causal reasoning, decision framework
• Action Layer for runbook engine, API executor, guardrail system, audit logger
• SOC 2 Type II certified
• Zero Data Retention
• Role-Based Access Control
• Audit Logging
• Private Deployment (VPC isolation)
• Encrypted Transit (TLS 1.3)
• Human-in-the-loop for critical actions
• Configurable autonomy levels per environment
• Automatic rollback on metric degradation
• No lateral movement outside defined scope
• Transparent reasoning at every step
• Agentic instrumentation
• Continuous monitoring of service health metrics and SLO burn rates
• Correlates deployment events with emerging anomalies
• Surfaces risky changes before they impact end-user experience scores
• Traverses Smartscape dependency chains automatically to isolate fault origin
• Delivers plain-language RCA enriched with topology context
• Analyzes host unit consumption, instrumentation coverage, and synthetic test gaps
• Identifies over-monitored or redundant host units
• Surfaces services or endpoints missing coverage
• Recommends synthetic monitor additions
• Pattern deviation detection across high-volume log indexes in real time
• Correlates log anomalies with deployment and change events automatically
• Surfaces emerging risk to on-call teams before notable events fire
• Cross-index correlation
• Automatic timeline reconstruction using event timestamps
• Plain-language RCA delivered to incident channel
• Analyzes indexing patterns, search load, and alert coverage
• Identifies indexes consuming the most license volume with lowest alert return
• Surfaces applications or services with missing log coverage
• Recommends search optimization and data retention right-sizing
• Anomaly detection across CloudWatch metrics, EKS cluster health, and Lambda error rates
• Deployment correlation: links new releases to emerging metric shifts
• Configuration drift detection across IAM, security groups, and VPC settings
• Cross-service signal correlation: CloudWatch + EKS + RDS + Lambda in a single analysis
• Automatic deployment-to-incident timeline reconstruction
• Plain-language root cause delivered within minutes of incident onset
• Right-size EC2 and RDS instances based on actual utilization patterns
• Identify idle Lambda functions and over-provisioned ECS tasks
• Surface services with missing CloudWatch alarms or incomplete log coverage
• Incident Awareness (real-time processing, bi-directional sync, custom routing, priority mapping)
• Intelligent Triage (auto-categorization, deduplication, alert correlation)
• Automated Diagnosis (runbook execution, log analysis, metric correlation)
• Smart Escalation (context enrichment, team routing, on-call awareness)
• Auto-Resolution (remediation actions, status updates, post-mortem data)
• Analytics & Insights (MTTR tracking, trend analysis, custom dashboards)
• Anomaly detection across all connected Grafana data sources simultaneously
• Risky deployment correlation: links code changes to emerging metric shifts
• Proactive alerts surfaced in Slack, PagerDuty, or preferred channel
• Cross-source signal correlation: metrics + logs + traces in a single analysis
• Automatic timeline reconstruction from annotations and alert events
• Plain-language RCA delivered within minutes of incident onset
• Analyzes dashboards, data source query volumes, and retention settings
• Identify over-queried or redundant dashboard panels burning query budget
• Surface services with no dashboards or missing SLO coverage
• Recommend metric cardinality reductions and retention right-sizing
• Long-running query trend detection before pipeline SLA breach
• Warehouse queueing and concurrency exhaustion risk identification
• Credit consumption anomaly detection before billing period ends
• Long-running and failed query causality tracing
• Warehouse credit spike root cause identification (query, schedule, or concurrency)
• Data pipeline task failure correlation with Snowflake performance events
• Over-provisioned warehouse tier identification
• High-credit-consumption query pattern analysis
• Idle warehouse and unused table storage identification

Use cases

• Detecting, investigating, and resolving production incidents autonomously
• Preventing issues before they impact customers
• Automating root cause analysis
• Correlating telemetry and surfacing risks early
• Reducing Mean Time To Resolution (MTTR)
• Operating production systems autonomously
• Preventing regressions from shipping blind through change intelligence
• Investigating behavior drifts before alerts fire
• Executing remediation actions (scaling, rolling back, restarting)
• Grouping, deduping, and ranking noisy alerts
• Optimizing cloud spend by hunting idle capacity and misconfiguration
• Autonomous incident investigation for SRE teams
• Real-time investigation workflows
• Intelligent triage and routing of incidents
• Predicting risk and suppressing alert noise
• Taking autonomous preventive actions
• Automated incident response and closure
• Detecting risk before it becomes an incident
• Investigating in real time and delivering root cause
• Running production autonomously between incidents
• ML-powered detection of unusual patterns
• Automated grouping of related alerts
• Predictive alerting to identify issues before user impact
• Tracing issues across services to pinpoint source
• Assessing blast radius and affected customers
• Aggregating relevant logs, metrics, and traces automatically
• Executing runbooks with full audit trails
• Safe remediation with guardrails
• Human-in-the-loop for low confidence actions
• Continuous monitoring of service health and SLOs
• Correlating deployment events with anomalies
• Surfacing risky changes before impact
• Traversing dependency chains to isolate fault origin
• Delivering plain-language RCA
• Optimizing observability spend and coverage
• Detecting risk patterns in log streams
• Correlating log anomalies with changes
• Surfacing emerging risk to on-call teams
• Cross-index correlation for root cause from logs, metrics, traces
• Automatic timeline reconstruction from Splunk events
• Optimizing Splunk index usage and cost
• Detecting AWS degradation before P1 incidents
• Root cause analysis across full AWS environment
• Reducing AWS spend and closing observability gaps
• Automated triage, diagnosis, and resolution of PagerDuty incidents
• Reducing PagerDuty alert noise
• Automated escalation of unresolved incidents in PagerDuty
• Detecting degradation before Grafana dashboards turn red
• Root cause analysis from Grafana metrics, logs, and traces
• Optimizing Grafana usage patterns for cost and reliability
• Detecting Snowflake performance and cost issues
• Identifying root cause for Snowflake incidents or cost spikes
• Reducing Snowflake costs and improving pipeline efficiency

AI approach

NeuBird AI acts as a production operations agent that detects, investigates, and resolves production incidents autonomously. It applies advanced context engineering and deep domain expertise to correlate telemetry, surface risks early, and automate root cause analysis securely across hybrid cloud environments. The platform uses ML-powered detection for unusual patterns, causal reasoning, and a decision framework. It emphasizes that sensitive data never reaches an LLM, and LLMs are used for reasoning and pattern recognition only, with local execution of strategies against real data within the customer's VPC.

Tech named: ML-powered detection, context engineering, deep domain expertise, causal reasoning, decision framework, GenDB

What it says sets it apart

• Production operations agent that detects, investigates, and resolves incidents autonomously
• Applies advanced context engineering and deep domain expertise
• Correlates telemetry, surfaces risks early, and automates root cause analysis securely across hybrid cloud environments
• One agent covers the work of an eight-person war room
• Ships with battle-tested operating skills out of the box
• Prevents issues before they reach customers, reducing 3am calls
• Cuts resolution time from hours to minutes (92% MTTR reduction)
• Runs production autonomously between incidents, cutting cost and recovering engineering hours
• Works with existing stack, no rip-and-replace required
• Flexible pricing without hidden fees, unlimited alerts included
• Only pays when agents actively investigate incidents, perform analysis, or answer questions
• No ingest fees, no storage fees, no surprise bills
• SOC 2 Type II certified
• Optional deployment inside customer's own cloud environment (VPC/VNET) for data residency
• Data stays in your VPC, no telemetry sent to LLMs
• Sensitive identifiers stripped before context is shared with LLMs
• LLM advises on strategy; GenDB executes queries inside VPC
• LLMs used for reasoning and pattern recognition only, not code generation for product
• Secure by architecture, not by policy, with GenDB enforcing security model
• VPC Isolation, Role-Based Access Control, Full Audit Trail, Approval Gates, Blast Radius Limits, Encrypted in Transit
• Responsible AI with explicit scope limitations, human-in-the-loop for critical actions, configurable autonomy levels, automatic rollback, transparent reasoning
• AI SRE acts like an engineer, reasoning across metrics, logs, traces, topology, and changes to produce a single clear answer
• Observability agent fixes observability at its source, generating the right signals and taking autonomous preventive action
• Shifts work left from response to prevention, addressing root causes of alerts
• Enhances existing observability tools like Datadog, Splunk, Grafana, Dynatrace, AWS native tools rather than replacing them
• Provides cross-tool correlation beyond single-vendor data
• Delivers plain-language root cause analysis in Slack or terminal
• Offers license cost optimization recommendations for observability platforms
• Connects via IAM roles with read-only permissions for AWS, no agents to install
• Supports multi-account AWS architectures via AWS Organizations
• Connects to Snowflake using a read-only service account, no configuration changes required
• Provides automated rightsizing analysis with credit impact for Snowflake
• 5 minutes average setup time for integrations
• 24 hours to full system understanding for learning phase
• 24/7 autonomous monitoring