Network observability hasn’t kept pace with network complexity
Modern networks no longer look anything like the architectures legacy monitoring tools were built for. Today’s environments span on-premises infrastructure, multi-cloud services, SD-WAN, WiFi, optical transport, and edge devices — all generating metrics, logs, flows, and events around the clock. Operations teams are expected to keep all of it running smoothly, but most are working with tools that were never designed for this level of scale or complexity.
Tool sprawl and siloed data
Network, infrastructure, and application teams each rely on their own monitoring stack, leaving no single, correlated view of what's actually happening across the environment.
Alert fatigue
Thousands of disconnected, duplicate, or low-value alerts bury the handful that actually matter, and on-call engineers spend more time triaging noise than fixing problems.
Manual root cause analysis
When something breaks, engineers are pulled into war rooms to manually cross-reference dashboards, logs, and tickets across multiple tools just to find where a problem started.
Reactive operations
Without predictive insight or historical context, teams are stuck responding to outages after customers are already affected, rather than catching the early signals beforehand.
Rising MTTR and operational cost
Every hour spent hunting for root cause is an hour of degraded service, lost productivity, and mounting pressure on already-stretched operations teams.
These challenges compound as environments grow. Traditional, rules-based monitoring platforms require thousands of static thresholds and manual correlation logic just to keep up — an approach that breaks down the moment network and application architectures evolve faster than the rules written to monitor them.
How Selector Helps
In large-scale digital infrastructure environments, excess alert volume is more than an operational nuisance. It slows response, increases inconsistency, and raises the risk that important issues get lost in the noise. That was the challenge facing this customer as it worked to improve datacenter operations across interconnected systems, services, and facilities.
AI-native, not rules-based
Where legacy platforms require thousands of manually written and maintained regex patterns and static thresholds, Selector uses machine learning to automatically baseline normal behavior (accounting for time-of-day and seasonal cyclicity), detect anomalies, and extract meaning from unstructured logs — without an army of engineers tuning rules.
Automated correlation vs. manual triage
Incident investigation with legacy tools relies heavily on engineers manually cross-referencing dashboards across multiple systems to confirm anomalies. Selector's correlation engine runs continuously in the background, connecting metrics, logs, events, and topology — temporally and contextually — to tell the story of what happened, when, and why, automatically.
True full-stack visibility, not point solutions
Many competing tools specialize in a single domain — network, application, or infrastructure — forcing teams to stitch together visibility themselves. Selector delivers correlated visibility across the full operational path, spanning network, infrastructure, applications, SD-WAN, WiFi, optical, and cloud in one platform.
A living Digital Twin and historical DVR, not static diagrams
Legacy topology views are typically static and manually maintained. Selector continuously builds and updates a live Digital Twin of the environment, paired with Network DVR — the ability to rewind and replay historical network and device states for deep post-incident analysis.
Conversational AI, not rigid query languages
Rather than requiring engineers to learn proprietary query syntax, Selector's GenAI Copilot — powered by a purpose-built Network Language Model (NLM) — lets anyone ask questions in plain language and get expert-level answers, visualizations, and guided remediation steps.
Open and extensible, not closed
Selector is designed to plug into the tools organizations already use — ServiceNow, Splunk, PagerDuty, Slack, AWS, and hundreds of others — enhancing existing workflows rather than forcing a rip-and-replace.
Capability comparison
method
analysis
correlation
awareness
analysis
insight
Key use cases
Selector supports a broad range of operational use cases across the network and IT stack — all built on the same unified data and correlation foundation.
Noise Reduction
(L1–L7)
Historical Playback
Troubleshooting with
GenAI Copilot
Topology Analytics
& Analytics
Ticketing & Analytics
Key capabilities & features
AI-Powered Incident Management
- Multi-domain event correlation that connects alerts and events across systems into contextualized, actionable incidents.
- Intelligent prioritization that deduplicates, enriches, and ranks events by business impact to cut alert fatigue.
- Knowledge graph analysis that computes correlation graphs and clusters incidents to reveal hidden relationships.
- Automated incident resolution for recurring issues, with auto-create and auto-resolve ticketing workflows.
Operational Twin & Network DVR
- Continuous, real-time topology mapping enriched with live telemetry from every layer of the stack.
- Interactive drill-downs into infrastructure, network, and service layers.
- Time-slider navigation to step back in time and replay historical device states and conditions.
- Timeline-based root cause analysis to pinpoint and diagnose recurring issues.
GenAI Copilot & Network Language Model
- Natural language interface for ad-hoc queries against telemetry, logs, and alerts.
- Expert-level guidance and analysis powered by Selector's proprietary Network LLM (NLM).
- On-demand, ad-hoc visualization generation for faster troubleshooting.
- AI-powered guided remediation recommendations that accelerate resolution.
Full-Stack Data Collection & Correlation
- Protocol-agnostic telemetry collection via SNMP, gNMI/gRPC, NetFlow/IPFIX, BGP, Syslog, streaming telemetry, and more.
- Device auto-discovery with customizable collection profiles and CMDB/inventory integration.
- ML-driven log mining that clusters and extracts structured meaning from raw logs without manual regex rules.
- ML-driven baselining that accounts for time-of-day and seasonal patterns to flag genuine anomalies.
Open, Extensible Integration Ecosystem
Operational data from network equipment
Routers & switches (Juniper, Cisco, Arista, Ciena), infrastructure (VMware, Kubernetes), CMDB (Netbox, Nautobot, Infoblox), SD-WAN (Cisco Meraki, Palo Alto CloudGenix), and WLAN (Cisco Prime).
Alerting & collaboration
BigPanda, ScienceLogic, PagerDuty, Opsgenie, Slack, and Microsoft Teams.
Identity & event streaming
Okta, Ping Identity, Azure AD, Kafka, and file-based ingest (CSV, Excel, Google Sheets).
Real-time traffic insights & synthetic probes
Cisco ThousandEyes, Pingmesh, Traceroute, and custom synthetic probes.
Automation & workflow
ServiceNow, RunDeck, PagerDuty, Bitbucket, Jira, Itential, and SolarWinds.
Public cloud & application monitoring
AWS SQS, AWS CloudWatch, and Dynatrace.
Logs
Logs: Splunk, native Syslog, TACACS logs, and Logstash.
Key benefits & outcomes
Organizations that adopt Selector see measurable improvements in how quickly they detect, diagnose, and resolve issues — and in how much operational burden their teams carry day to day.
Faster MTTR
Automated correlation and root cause analysis collapse alert storms into clear, actionable insights — cutting mean time to detect, identify, and repair.
Proactive Operations
Anomaly detection and predictive 'what-if' analytics help teams catch and address risk before it affects end users.
Tool & Vendor Consolidation
A single, correlated platform reduces the need for multiple overlapping monitoring tools — and the cost that comes with them.
Fewer, Better Alerts
Deduplication and intelligent prioritization mean engineers see the incidents that matter, not thousands of disconnected notifications.
Reduced Operator Fatigue
Less time spent triaging noise across siloed tools means more time spent on optimization and strategic work.
Faster Time to Value
Open, API-first integrations mean Selector plugs into existing workflows and tools quickly, without operational disruption.
The bottom line
Network observability shouldn’t mean stitching together a dozen siloed tools and hoping an engineer can connect the dots before customers feel the impact. Selector brings full-stack visibility, AI-driven correlation, and natural language troubleshooting into a single platform so operations teams spend less time hunting through dashboards and more time keeping services healthy.
Unlike legacy monitoring and point observability tools, Selector is AI-native from the ground up: it learns what normal looks like, surfaces the few incidents that matter out of thousands of raw signals, and points teams straight to root cause. And because it’s open and extensible, it enhances the tools and workflows you already rely on rather than forcing a rip-and-replace.
73%
Lower MTTR
10x
Faster RCA
95%
Noise reduction
70%
Fewer incidents