Visibility gaps
Incomplete signals leave users discovering failures first.
Observability & AIOps Systems
Connect unified telemetry, service semantics, incident workflows, and intelligent analysis into a reliability operating system.
Unified telemetrySemantic correlationIncident loopAIOps analysisOperations agentsSLO and FinOps
Customer challenges
Recover complete operating context from fragmented signals
Fragmented data and tools
Telemetry, changes, and tickets lack shared semantics.
Alert noise
Duplicate and derived alerts hide actionable incidents.
Experience-dependent diagnosis
Experts manually assemble impact and cause evidence.
Capability chain
Unify evidence, understand relationships, and drive reliable response
- 01Collect
Unify metrics, logs, traces, profiles, and traffic.
- 02Standardize
Create a shared language through labels, models, and quality rules.
- 03Correlate
Connect services, resources, changes, owners, and business context.
- 04Analyze
Use rules, models, and topology algorithms to identify anomalies and candidates.
- 05Decide
Produce reviewable recommendations from impact, SLOs, and operating knowledge.
- 06Close the loop
Connect incidents, tickets, response, review, and improvement.
Service scope
Nine work packages across observability, reliability, and intelligent operations
01
Observability strategy and data model design
Define objectives, layered models, labels, tool boundaries, and the roadmap.
An executable blueprint connecting data to operations.02
Unified collection of metrics, logs, traces, profiles, and traffic
Build collection, processing, routing, storage, retention, and data-quality controls.
A consistent and traceable observability data foundation.03
Application performance and business observability
Implement APM, real-user, transaction, SLO, and service-health views.
Application health and impact expressed through business semantics.04
Infrastructure, cloud-native, and middleware observability
Unify hosts, networks, storage, Kubernetes, databases, and middleware observability.
A unified operational view from resources to services.05
Semantic correlation, call topology, and business maps
Build call topology, resource relationships, business maps, and semantic models.
Fragmented signals become verifiable operational context.06
Alert, event, and incident management systems
Build event aggregation, severity, routing, response, escalation, and review loops.
A standardized incident system from signal to review.07
AIOps anomaly detection, correlation, and root-cause analysis
Build anomaly detection, event correlation, topology impact, and root-cause candidate evidence.
Explainable and reviewable anomaly and root-cause analysis.08
Capacity, performance, and FinOps optimization
Build forecasting, performance baselines, allocation, cost analysis, and optimization loops.
Continuous optimization within service-level constraints.09
LLM enhancement and intelligent operations agent engineering
Build retrieval, tool use, orchestration, evaluation, access, and audit controls.
Faster operations collaboration within controlled boundaries.Coverage
Cover the full runtime from infrastructure to AI agents
01
Infrastructure
Hosts, networks, storage, and cloud resources
- Capacity and utilization
- Network and storage
- Resource health
02
Cloud native
Kubernetes, containers, clusters, and platform services
- Clusters and nodes
- Workloads
- Control plane
03
Middleware
Databases, messaging, cache, and API gateways
- Connections and throughput
- Dependencies
- Service quality
04
Applications and business
APM, real users, transactions, and SLOs
- User experience
- Transactions
- Service objectives
05
Data and AI platforms
Data pipelines, model services, GPU, and inference
- Data jobs
- Inference performance
- Resource cost
06
Agent systems
Model calls, retrieval, tools, and task traces
- Models and tokens
- Retrieval quality
- Tool execution
Semantic correlation center
Turn fragmented telemetry into verifiable service and business relationships
- 01 Source layer
Connect telemetry, CMDB, changes, tickets, and business data.
- 02 Processing and quality
Collect, clean, normalize, align, and govern data quality.
- 03 Semantic model
Model applications, services, resources, business, owners, and changes.
- 04 Correlation and analysis
Build verifiable evidence through graph relations, topology, rules, and algorithms.
- 05 Scenario services
Support incidents, change, capacity, cost, SLOs, and intelligent operations.
Incident and reliability loop
A standard process from detection to learning
- 01Detect
Identify anomalies with baselines and SLOs
- 02Converge
Deduplicate, suppress, and cluster alerts
- 03Correlate
Add topology, change, and business context
- 04Diagnose
Rank candidates, impact, and evidence
- 05Respond
Connect owners, tickets, and runbooks
- 06Learn
Capture knowledge and improve rules and models
AIOps
Improve analysis speed and consistency with algorithms and topology evidence
Anomaly detection
Find deviations, trends, and compound anomalies beyond static thresholds.
Alert reduction
Converge duplicate, derived, and common-cause alerts into incidents.
Root-cause candidates
Rank candidates using topology, timing, and change evidence.
Impact analysis
Use service and business relations to determine blast radius and priority.
Change risk
Compare releases with baselines, history, and dependencies.
Capacity and FinOps
Analyze capacity, performance, utilization, and cost within service targets.
LLM augmentation
Make operating evidence easier to understand and use
LLMs do not replace anomaly detection or root-cause judgment. Rules, models, and topology algorithms provide evidence; LLMs retrieve, explain, summarize, collaborate, and support controlled execution.
Incident analysis agent
Summarize incidents, candidates, impact, and evidence links.
Operations Q&A agent
Retrieve telemetry, topology, knowledge, and history within access controls.
Change risk agent
Explain related changes, risk signals, and recommended validation.
Operations reporting agent
Draft reports, briefings, and reviews with citations.
Target system
Five layers connect data, analysis, scenarios, and governance
- 01Unified operations entry
Role workspaces, service health, incidents, and intelligent interaction
- 02Scenario applications
Application observability, incidents, change risk, capacity, and FinOps
- 03Intelligent analysis
Anomaly detection, event correlation, topology, and candidates
- 04Semantic and data layer
Observation models, service topology, business maps, and unified data
- 05Governance and integration
Data quality, model evaluation, knowledge, access, audit, and integration
Implementation
A five-stage path from assessment to continuous operations
- 01Assess
Inventory tools, data, workflows, teams, and priority scenarios.
- 02Standardize and collect
Unify objects, labels, data quality, and collection architecture.
- 03Events and semantics
Build the event model, service topology, business map, and response loop.
- 04AIOps pilots
Validate algorithms, evidence, and operating measures in high-value scenarios.
- 05Agents and operations
Expand intelligent collaboration under access, evaluation, and audit controls.
Deliverables
Deliver the blueprint, standards, workflows, and validated scenarios
Objectives, capability map, technology path, phases, and investment boundaries.
Observation objects, labels, ingestion, quality, and cost policies.
Service, resource, change, ownership, and business relationships.
Severity, routing, collaboration, escalation, runbooks, and reviews.
Algorithms, evidence, scenario validation, and effectiveness baselines.
Knowledge, tools, access, evaluation, audit, and controlled execution.