Inducer AIOps
Integrated AI-enabled
managed service solution.
For telecom operators and private cloud infrastructure. A fully private, AI-driven platform for network intelligence and automation — from probe collection through deep-learning root-cause analysis to natural-language infrastructure change, with no external cloud AI anywhere in the path.
The challenge
Why telecom operators and cloud teams struggle today
Blind spots in complex infrastructure
Modern telecom environments span thousands of nodes across physical, virtual and cloud layers — creating visibility gaps that lead to undetected failures and slow incident response.
Reactive instead of proactive operations
Teams learn about incidents only after customers are impacted. Manual investigation drains expensive engineering resource on repetitive, low-value analysis.
External AI means sovereignty risk
Most AI monitoring solutions require sending sensitive network telemetry to external cloud providers — violating telecom compliance, sovereignty regulation and security policy.
Tool sprawl & integration complexity
Fragmented toolchains with separate monitoring, logging, tracing and ITSM tools create data silos, slow root-cause analysis and drive up operational overhead.
Solution overview
A fully private, AI-driven platform for network intelligence and automation
Five layers, deployed entirely inside your infrastructure. Each one is independently useful; together they close the loop from anomaly to remediated change.
Data & probe collection
SNMP, NetFlow, sFlow, NETCONF/YANG, gRPC, OpenTelemetry and Prometheus — on-premises only.
AI-powered processing
Kafka and Flink stream processing, feature engineering, and multi-tier storage across InfluxDB, Elasticsearch and Neo4j.
AI analytics & intelligence
LSTM, autoencoder, GNN and isolation forest models. Root-cause analysis. Predictive failure detection.
Multi-LLM AI agents
Self-hosted Llama 3 and Mixtral-class agents: network, security, performance and compliance specialists.
IaC automation platform
Natural language to Terraform, Ansible and Kubernetes. GitOps, canary deployments and automatic rollback.
Layer 01
Collection built for coverage, not convenience
Protocol-level acquisition across the network, the infrastructure, the applications and the hardware — plus log collection and on-demand distributed tracing when an incident needs depth rather than breadth.
SNMP v2c / v3
Device metrics & traps
NetFlow v9 / IPFIX
Traffic flow analysis
sFlow
Packet sampling
NETCONF / YANG
Config telemetry streaming
gRPC telemetry
Model-driven real-time data
OpenTelemetry
Traces, metrics, logs
Prometheus
Container monitoring
IPMI / JMX
Hardware & JVM health
Log collection
- Syslog RFC 5424 / 5425 over UDP, TCP and TLS
- File-based agents with log rotation
- Kubernetes and Docker container logs
- Windows Event Logs via WMI
- Database audit: PostgreSQL, MySQL, Oracle, MongoDB
On-demand tracing
- OpenTelemetry and Jaeger compatible
- Span correlation across microservices
- L3/L4 packet flow visualisation
- Adaptive sampling strategies
- APM code-level instrumentation
Layer 03
Self-hosted ML on your own GPU infrastructure
Three tiers of detection running together, reconciled into a single root-cause narrative — because a detector that cries wolf is worse than no detector at all.
Statistical methods
- Z-score and IQR outlier detection
- ARIMA / SARIMA time-series forecasting
- Prophet for seasonal patterns
ML models
- Isolation Forest for multivariate anomalies
- One-Class SVM novelty detection
- Autoencoder reconstruction-error scoring
Deep learning
- LSTM sequence anomaly detection
- Variational autoencoders (VAE)
- Graph neural networks (GNN)
- Transformer multi-modal analysis
Anomaly detection categories
Performance
Latency spikes, CPU/memory saturation, throughput drops
Network
Packet loss, routing anomalies, unusual traffic patterns
Security
DDoS, port scanning, unauthorised access attempts
Application
Error-rate increases, resource leaks, slow responses
Infrastructure
Hardware failures, disk saturation, temperature events
Root cause
Correlation across all categories, reducing MTTR
Layer 04
Agentic AI & multi-LLM architecture
Self-hosted, custom fine-tuned models. A master orchestrator decomposes the task, routes it to the domain specialist that should own it, and synthesises the response.
Master orchestrator agent
Task decomposition · Agent coordination · Response synthesis
Network analysis
BGP/OSPF · SDN · Routing · Traffic patterns
Security agent
CVE database · MITRE ATT&CK · Threat detection
Performance optimiser
Bottlenecks · Capacity planning · Tuning
Compliance & policy
PCI-DSS · SOC 2 · GDPR
MCP-powered RAG pipeline
Context intelligence assembled through the Model Context Protocol, so every agent reasons from the same current picture of the estate.
Vector store
Milvus, Qdrant or Weaviate holding embedded runbooks and network diagrams, retrievable in context at query time.
Incident knowledge base
Historical incidents embedded and searchable, so the system recognises the shape of a problem it has already solved.
Layer 05
Speak your intent — AI translates it to production-ready infrastructure code
Example natural language commands
- “Deploy the latest microservice to production with canary rollout”
- “Block all traffic from 10.5.0.0/16 due to an active security incident”
- “Scale up the web tier to handle the increased traffic load”
- “Roll back the database deployment to the previous stable version”
Terraform code generation
Reviewed as code, merged through the same pipeline as everything else
Ansible automation
Configuration and remediation playbooks generated from intent
Kubernetes manifests
Workload, policy and scaling definitions for the target cluster
GitOps workflows
Declared state in version control with a reviewable history
Drift detection
IaC compared continuously against actual state
Multi-stage approvals
Role-based approval chains before protected environments
Complete audit trail
Immutable, tamper-evident logs of every action
RBAC + ABAC
Granular per-resource permissions for humans and agents
Business impact
What changes once the loop closes
Reduced mean time to resolution through AI root-cause analysis. Less manual effort through intelligent automation. Data sovereignty by removing external dependencies. Monitoring that never sleeps.
Proactive problem prevention
Detect and resolve issues before customer impact using predictive ML models rather than threshold alerts after the fact.
Cost optimisation
Intelligent capacity planning prevents over-provisioning while keeping adequate headroom for real demand.
Enhanced security posture
Security agents trained on CVE data and MITRE ATT&CK, with automated threat detection across the estate.
SLA compliance assurance
Predictive monitoring keeps service levels above contractual obligation rather than reporting the breach afterwards.
Knowledge retention
Self-operating agents encode and apply organisational expertise continuously, so it does not leave with the engineer who had it.
Regulatory compliance
Built-in PCI-DSS, HIPAA, SOC 2 and GDPR monitoring with compliance reporting as an output rather than a project.
Why Inducer AIOps
Built for telecom-scale complexity. Designed for complete control.
Private by design
No external cloud AI dependencies. Fully deployed in your data centre. Air-gap option available.
Enterprise-grade scale
Millions of events per second ingestion. Sub-second query response. Auto-scaling Kubernetes foundation.
Domain expert AI agents
Specialised agentic AI fine-tuned on telecom, security, compliance and performance domains.
End-to-end automation
From anomaly detection to natural-language IaC generation — one unified, cohesive platform.
Integrations
Ready to transform your network operations?
Bring one domain.
We will close one loop.
The fastest proof is a single agent owning a single repeatable analysis task, running against your telemetry, inside your perimeter.