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. A typical DIY approach combines open-source and cloud components, such as Telegraf or gNMIc collectors, Kafka, Prometheus or InfluxDB, OpenSearch or Elasticsearch, Grafana, custom Python correlation logic, and LLM APIs, integrated and maintained by an internal team.
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.
DIY Stack
LLM scripts
Custom agents or chat interfaces built on LLM APIs.
Dashboards and alerts
Grafana dashboards and Alertmanager rules.
Custom correlation
Python rules, scripts, and models written in-house.
Data stores
Prometheus, InfluxDB, OpenSearch, and Kafka.
Collectors
Telegraf, gNMIc, Logstash, flow collectors, custom scripts.
Sources
Whatever the team integrates and keeps working.
Where a DIY stack falls short architecturally:
Every layer is the team’s to build and maintain: Collection, normalization, storage, correlation, and agents each need engineering, upgrades, and on-call ownership. Selector delivers all of them as one maintained platform.
Causation is the hardest part to build: Dashboards and alert rules are straightforward; deterministic, topology-aware causation across vendors is not. Selector’s patented models deliver it.
LLM-first shortcuts are fragile: LLM scripts over raw data can hallucinate and give different answers to the same question. Selector uses LLMs only to explain deterministic findings.
Use case 1: Observability
Multivendor data collection
Open-source collectors can gather most data types, but each needs integration and upkeep. Selector collects everything into one pipeline as a maintained product.
DIY gaps
Every collector, integration, and upgrade is owned by the internal team.
Topology and dependency modeling must be built from scratch.
Coverage drifts as vendors, firmware, and APIs change.
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. In a DIY stack, every parser, schema, and enrichment rule is written and maintained by the team.
Data Hypervisor
DIY gaps
Parsers, schemas, and enrichment logic are written and maintained by hand.
Data spreads across several stores with different query languages.
Raw-data replay of network state must be designed and built.
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. A DIY stack starts with thresholds and requires custom data science for anything more.
DIY gaps
Anomaly detection must be built, tuned, and retrained.
Cross-domain correlation requires custom models.
Prediction requires data science investment.
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
Selector example
The DIY reality
DIY gaps
Correlation is rules and scripts unless causal models are built.
Symptom-versus-cause labeling must be engineered.
LLM-based analysis can return different answers for the same data.
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
Foundry
DIY gaps
Agent governance, approvals, and audit must be built.
MCP servers, RBAC, and security must be engineered.
Ongoing ownership competes with operations work.
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 team gets on day one with Selector instead of building and maintaining them in-house.
1. One maintained platform
2. Deterministic diagnosis
Root cause comes from deterministic topology and domain models, so the same data yields the same answer.
3. Detection beyond thresholds
4. Streaming and routing depth
5. Full-fidelity history
6. No templates to maintain
7. Your playbooks as agents
8. Governed action through MCP
9. Custom outcomes with engineers
10. Reuse what you have
Selector ingests from existing collectors, Kafka, Prometheus, and InfluxDB, so prior DIY investment carries forward.
Selector patents driving innovation
Selector vs. Do It Yourself at a glance
The bottom line
Building in-house gives full control and works well for dashboards and alerting, but collection upkeep, normalization, correlation, causation, and agent governance become long-term engineering commitments.
Selector delivers those capabilities as a maintained platform, with patented causation, raw-data replay, governed agents, and forward deployed engineers, so the team spends its time operating the network instead of building tools.
30 min
Model retraining cycle
99.9%
Noise reduction
8x
Faster resolution per P1