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Technical comparison

Selector vs. LiveAction

Observability and Event Intelligence Use Cases

October 2026

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How each platform builds operational context

Selector collects raw telemetry and operational context together from every vendor, normalizes and enriches it before any model runs, and establishes causation with purpose-built ML before agents act. LiveAction focuses on network performance and traffic visibility: LiveNX for flow, SNMP, and SD-WAN analytics, LiveWire for packet capture, and ThreatEye for network detection and response.

Foundry agents

Rosetta, Loom, TicketMaster, Artist, Herald, Sculptor; MCP and A2A.

Collaboration

Natural language, dashboards, ChatOps, ITSM workflows, query-backed APIs.

Knowledge

Anomaly detection, correlation, causation, forecasting, knowledge graph.

Data Hypervisor

ML schema inference, enrichment, ELT raw preservation, routing.

Collection

Horizontal engines near the source; push and pull; independent scaling.

Sources

Any vendor’s devices, cloud, CMDB, IPAM, ITSM, tools, and files.

Analytics and alerts

LiveNX dashboards, alerts, and reports; ThreatEye detections.

Traffic analysis

Flow and application visibility, QoS, and SD-WAN analytics.

Packet layer

LiveWire packet capture and forensics.

LiveNX platform

Flow, SNMP, and API data store.

Collection

LiveNX nodes and LiveWire appliances; virtual or physical.

Sources

Routers, switches, SD-WAN controllers, firewalls, and packet capture points.

Where LiveAction falls short architecturally:

Traffic visibility, not causation: LiveAction excels at flow, application, and packet visibility; it does not establish cause across domains. Selector determines cause across network, cloud, and application data.

Network-centric scope: Cloud, application, CMDB, and log context sit largely outside LiveAction. Selector correlates them in one model.

Engineer-driven analysis: Investigation relies on engineers pivoting across flow, SNMP, and packet views. Selector correlates and explains incidents automatically.

Use case 1: Observability

Multivendor data collection

LiveAction is strong in flow, application, SD-WAN, and packet visibility. Selector adds streaming routing analytics, logs, configuration, and context in one pipeline.
Data type
Metrics
SNMP and API polling; SD-WAN controller data. Native gNMI streaming not described.
SNMP, gNMI and model-driven streaming telemetry, Prometheus-compatible sources, REST APIs.
Logs
Log analytics not described.
Syslog from any device, infrastructure, or application, structured by ML; Splunk, Logstash, TACACS.
Flow
Strong: NetFlow, IPFIX, sFlow, NBAR, and application recognition.
NetFlow, sFlow, IPFIX from devices or collectors, enriched with application, owner, and criticality.
Routing
BMP and IGP ingestion not described.
BGP, BMP/OBMP, and IGP state ingested from customer devices.
Configuration
Configuration collection and drift not described.
Running and intended config via NETCONF and CLI; drift correlated with impact.
Topology
Network topology and path views.
From SNMP, CDP/LLDP, configuration, and telemetry, across all vendors; digital twin.
Context
CMDB integration not described.
ServiceNow CMDB, NetBox, Nautobot, Infoblox; owner, site, circuit, criticality.
Hybrid & multicloud
Cloud flow and SD-WAN visibility.
AWS, Azure, Google Cloud APIs, flow logs, transit gateways, VPCs, correlated with on-prem.
Digital experience
Synthetic and application performance views.
ICMP and UDP probes, ThousandEyes, traceroute, custom tests.
Files / streams
Not described in LiveAction materials.
Kafka, CSV, Excel, Google Sheets.

LiveAction gaps

Configuration, log, and CMDB data largely outside scope.

BMP and IGP ingestion not described.

Native gNMI streaming not described.

Selector value

Streaming telemetry, flow, routing, configuration, and context from any vendor in one pipeline.

Routing changes tied to the services they affect, from BMP and IGP state on the customer’s own devices.

Ingestion from files and streams for data with no API.

Use case 1: Observability

Data normalization and enrichment

Selector normalizes and enriches every signal before any model runs. LiveAction structures data around flow and SNMP schemas.
Capability
Data Hypervisor
Schema inference
Flow and SNMP schemas. ML schema inference not described.
ML infers structure from free-form logs, syslog, traps, and CLI output; no parsing templates.
Entity resolution
Devices, interfaces, sites, and applications.
Named-entity and clustering models group events across vendor and format variations.
Enrichment
Application, site, and QoS mappings.
Topology, dependency, owner, site, maintenance state, and criticality added before analysis.
Data fidelity
Raw flow retained for a period, then summarized.
ELT: raw data lands with original timestamps; transformation follows.
Noise suppression
Alert thresholds.
Duplicate, flapping, and irrelevant signals removed before the intelligence layer.
Business logic
Alert policies and reports.
Maintenance windows, provider notices, suppression, and criticality in YAML via DevOps workflows.
Customization
Custom applications and dashboards.
S2ML tunes enrichment, labeling, noise rules, prioritization, and LLM output.
Data access
REST API and reports.
Every view is a query and can be exposed as an API endpoint; MCP server.

LiveAction gaps

Structure follows flow and SNMP schemas; free-form data not interpreted.

Enrichment centers on application and site mappings.

Long-term history is summarized.

Selector value

Learned normalization: no parsing templates or rules to maintain.

Raw records retained with original timestamps for replay and re-analysis.

One enrichment layer applies topology, ownership, maintenance, and criticality to every signal.

Use case 1: Observability

AI-powered operational analytics

Selector models every time series individually and correlates across domains automatically. LiveAction detects issues through traffic baselines and thresholds.
Capability
Anomaly detection
Traffic baselines and thresholds; ThreatEye behavioral analytics.
Self-supervised, statistical, and regression models. A model per time series, retrained every 30 minutes, with seasonality.
Log analysis
Not described.
Small language models. Log semantics interpreted, clustered across vendors, and turned into structured events.
Prediction
Capacity planning reports. Optical failure prediction not described.
Forecasting and predictive models. Capacity forecasting and optical transceiver failure prediction.
Cross-domain analysis
Network, application traffic, and packet views.
One model across domains. Correlation runs automatically across network, cloud, and application signals.
Natural language
Natural-language investigation not described.
Rosetta. Plain-language questions answered with every claim linked to source data.

LiveAction gaps

Detection centers on thresholds and traffic baselines.

Cross-domain correlation not described.

Optical failure prediction not described.

Selector value

Per-series models retrained every 30 minutes detect anomalies no threshold would catch.

Automatic correlation across network, cloud, and application signals in one model.

Optical failure prediction and capacity forecasting before impact.

Use case 2: Event intelligence

Event correlation and root cause

LiveAction provides deep traffic evidence, including packet capture, but correlation and root cause are left to engineers. Selector correlates events and determines cause automatically.
Capability
Inputs
LiveAction alerts.
Alerts and the raw telemetry, logs, flow, and configuration behind them, correlated together.
Correlation method
Alert correlation not described.
Contextual embeddings and clustering by shared devices, interfaces, IP addresses, topology, and time.
Noise reduction
Threshold tuning.
Up to 99.9% of irrelevant noise removed before correlation.
Root cause
Engineer-driven analysis across flow and packet data. Causation model not described.
Deterministic causation from topology and domain logic: reboot, then link failure, then BGP flap.
Symptom vs. cause
Not described in LiveAction materials.
Downstream effects labeled as consequences, not separate faults, across every domain.
Change correlation
Not described.
Configuration diffs and change events correlated directly with degradation.
Evidence and replay
Packet capture for forensic replay of traffic. Network state replay not described.
Every finding links to source data; Network DVR replays past network state.

Selector example

The Loom agent resolved 214 alerts across six tools to a single fiber cut on one long-haul span, labeled routing reconvergence and cloud failover as consequences, and flagged the secondary path at 78% utilization as the next risk.

Where LiveAction helps

Packet and flow data can show what traffic did during an incident. Connecting that evidence to a device reboot, routing change, or cloud failover is left to the engineer.

Kentik gaps

Alert correlation and causation not described.

Investigation is engineer-driven across views.

Network state replay not described.

Selector value

Alert floods collapse into one incident with a named cause, consequences, and next risk.

Telemetry, logs, flow, and configuration correlated with alerts in one model.

Every finding is traceable and reproducible with Network DVR.

Use case 2: Event intelligence

Agents, ITSM, and governed action

Function
Foundry
Investigation
Dashboards and drill-downs.
Rosetta. Interprets plain-language intent, dispatches agents across domains, returns one cited thread.
Correlation / RCA
Not described.
Loom. Connects time, topology, changes, and domains into one incident with a root cause.
ITSM
ServiceNow integration.
TicketMaster. Bi-directional ServiceNow sync across the lifecycle, with per-customer field mapping and incident-aware deduplication.
Communications
Reports.
Herald. Executive briefs, ops updates, and post-incident reports from the verified record.
Custom agents
Not described.
Sculptor. Turns a described playbook into a governed agent with goal, tools, triggers, and approval points.
Action
Automated remediation not described.
Agents investigate, act, and verify within operator-set limits on high-confidence findings.
MCP
Not described.
Production MCP server with OAuth2, granular RBAC over insights, recommendations, and actions.
Deployment
Physical, virtual, and cloud appliances.
SaaS, on-premises, customer cloud, or air-gapped.

LiveAction gaps

Agentic investigation not described.

Customer-defined governed agents not described.

An MCP server not described.

Selector value

Sculptor builds governed agents from the team’s own playbooks, acting only on verified findings.

MCP server shares insights and governed actions with other agent platforms.

Every Foundry agent is available now, in every deployment model.

Functionality gained with Selector

Capabilities a LiveAction customer adds with Selector, whether Selector runs alongside LiveAction or replaces it.

1. From visibility to causation

Selector turns network, cloud, and application data into a named root cause automatically, instead of leaving engineers to pivot across traffic views.

2. Deterministic diagnosis

Root cause comes from deterministic topology and domain models, so the same data yields the same answer.

3. Detection beyond thresholds

Per-series models retrained every 30 minutes find conditions no static threshold or rule would catch.

4. Streaming and routing depth

gNMI streaming telemetry and BMP/OBMP and IGP analytics from the customer’s own devices, plus optical failure prediction.

5. Full-fidelity history

Raw data preserved with original timestamps; Network DVR replays past network state so findings can be verified and re-run.

6. No templates to maintain

Learned normalization infers structure across vendors and formats, replacing parsing rules and custom collection scripts.

7. Your playbooks as agents

Sculptor turns the team’s own runbooks into governed agents with approved tools, triggers, approval points, and audit.

8. Governed action through MCP

Selector’s MCP server exposes insights, recommendations, and actions to other agent platforms under OAuth2 and RBAC.

9. Custom outcomes with engineers

Forward deployed engineers build integrations, data models, views, and agents on the customer’s own data.

10. No rip-and-replace

Selector deploys alongside LiveAction and ingests from existing tools and APIs, adding causal root cause and governed agents above them.

Selector patents driving innovation

The techniques that define the Selector platform are covered by Selector’s U.S. patent portfolio. Patent records are available on request.
Capability
What Selector’s patented approach delivers
Root cause analysis
Deterministic causation across every domain.
Reproducible investigations
Network DVR replays network state so an investigation can be re-run.
Log intelligence
Structured signal from any vendor’s syslog without parsing templates.
Conversational investigation
Learns each customer’s names for sites, services, and devices without manual labeling.
Custom outcomes on demand
Declarative query chains for drill-downs and customer-specific views.
Operational context
Maintenance-window-aware alerting and reporting.
Optical failure prediction
Flags failing optics before they cause an outage.
Capacity planning
Forecasts capacity limits before they affect services.
Scale
Efficient storage and querying of millions of data points per minute.

Selector vs. LiveAction at a glance

Capability
Observability
Data collection
Strong flow, SD-WAN, and packet visibility.
Any vendor, including gNMI, BMP/IGP, flow, config, and file sources, in one pipeline.
Normalization
Flow and SNMP schemas.
Learned schema inference; ELT raw preservation.
Detection
Baselines and thresholds.
Per-series models retrained every 30 minutes.
Cross-domain analysis
Network and traffic views.
Automatic correlation in one model.
Event intelligence
Correlation
Not described.
Alerts plus the raw telemetry behind them.
Root cause
Engineer-driven.
Deterministic causation with symptom labeling.
Reproducibility
Packet forensics.
Network DVR replay; source-linked evidence.
Custom agents
Not described.
Sculptor builds governed agents from playbooks.
MCP
Not described.
Server for insights, recommendations, and governed actions.
Deployment
Appliances, virtual, cloud.
SaaS, on-premises, or air-gapped.

The bottom line

LiveAction is a strong network traffic visibility platform with deep flow, application, SD-WAN, and packet analysis.

Selector is built for a different starting point. It correlates telemetry, configuration, logs, and context from every vendor in one model, determines cause automatically, and puts governed agents on top, including agents built from your own playbooks.

30 min

Model retraining cycle

99.9%

Noise reduction

8x

Faster resolution per P1

See Selector on your own data

www.selector.ai | sales@selector.ai

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