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One causal entity graph. Four AI engines. Built for enterprise engineering teams who need answers, not dashboards.

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Applicare enables teams to observe, automate, and resolve incidents faster across every stack.

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Solutions · Red Hat OpenShift

Understand every OpenShift incident in under 60 seconds.

From clusters and operators to routes, pods, and customer transactions, Applicare identifies root causes, explains business impact, and recommends remediation automatically.

Applicare — OpenShift Incident Intelligence⬤ Live
Request Path
🔀 Route
🔀 Service
📦 Pods
🗄 Database
🛒 Checkout
Pod Memory
97%
checkout-api · OOMKill risk
API Latency
2,640ms
p99 ↑ 17× · errors 4.1%
✓ Root Cause Identified — Deploy checkout-api:v2.7 raised heap usage 3×; pods hitting memory limit and restarting. Cascading API latency → checkout failures. → Recommended: roll back to v2.6 or raise memory limit + fix leak.
<60s
Root cause across the full OpenShift stack
99.99%
Application availability for Applicare customers
82%
Lower MTTR on cluster incidents
96%
Fewer Sev-1 incidents after deployment
Trusted for mission-critical OpenShift environments
☁️
Multi-cluster OpenShift support
🏢
Enterprise-scale deployments
🔗
Real-time dependency mapping
🧠
AI-assisted incident investigation
Automated remediation recommendations
The Observability Gap

Monitoring tells you a pod failed.
Applicare tells you why.

OpenShift monitoring shows you that a pod restarted. Applicare tells you what changed, why it failed, which services are affected, how customers are impacted, and what to do next.

Traditional monitoring tells you
  • A pod restarted
  • CPU / memory increased
  • Error rate increased
You still have to find why — manual correlation across kubectl, dashboards, and logs while customers are impacted.
Applicare tells you
  • What changed (deploy, config, scaling event)
  • Why it failed (root cause pod, container & resource)
  • Which services are affected and in what order
  • How customers are impacted — and what to do next
Causality, business impact, and remediation — in under 60 seconds.
Reference Architecture

From route to customer impact — one causal path.

Applicare follows the request through every OpenShift layer and connects it to the causality engine and recommended actions.

🔀 Route
🔀 Service
🚀 DeploymentConfig
📦 Pods
🗄 Database
🛒 Customer Transactions
🧠 ArcIn Causality Engine
⚡ Recommended Actions
Incident Investigation

See how Applicare investigates incidents.

10:03 AM
New deployment released
checkout-api:v2.7 rolled out via DeploymentConfig to the prod cluster
10:05 AM
Pod memory consumption spikes
checkout-api pods climb 62% → 97% of limit · OOMKills begin
10:06 AM
API latency increases
p99 latency: 154ms → 2,640ms as pods restart under load
10:07 AM
Checkout failures begin
Checkout error rate +4.1% · customer transactions failing
10:08 AM
🧠 ArcIn identifies root cause
Memory leak in v2.7 · pods exceeding limit · 3-service cascade confirmed
10:09 AM
⚡ IntelliTune recommends rollback
Option A: roll back to v2.6 (no downtime). Option B: raise memory limit + patch leak
ArcIn — Causality Analysis⬤ Resolved 10:09
🧠 ArcIn Root Cause:
Deploy checkout-api:v2.7 introduced a heap leak. Pods crossed their 512Mi memory limit, triggering repeated OOMKills and restarts. Under load, request queues backed up — API latency and checkout failures cascaded in 4 minutes.
⚡ IntelliTune:
Recommended: roll back to v2.6 immediately (zero downtime), then ship leak fix. Estimated resolution: 3 minutes.
Traditional Monitoring
6–20
minutes to understand
Applicare
<60s
to root cause + remediation
Entity Graph

Every dependency. One graph.

Applicare continuously maps the relationships between clusters, nodes, projects, DeploymentConfigs, services, pods, APIs, databases, and customer transactions — so teams immediately understand how a failure propagates across the environment.

Live Entity Graph — OpenShift
Cluster (prod-us-east-1)Healthy
└─ project ──────────────────
Deployment (checkout-api)Restarting
└─ runs ─────────────────────
Pod (checkout-api-7c9…)🔴 OOMKilled
└─ calls ────────────────────
Database (orders-db)Conn ↑
└─ impacts ──────────────────
Customer Checkout FlowFailing 4.1%
└─ analysed by ──────────────
ArcIn Causality Engine✓ Root Cause + Fix
Auto-discovered in 9.8s · Updated every 15s · 1,240 entities mapped
🔗
Cluster-to-customer topology
Clusters, nodes, projects, DeploymentConfigs, services, pods, APIs, databases, and user transactions linked in one living graph — incidents spanning five objects surface as one root cause.
Auto-discovered · no manual mapping
Deploy-correlated intelligence
Every rollout, scaling event, and config change tracked against baselines. Regressions caught within seconds — before the pager fires.
Rollback triggers · deployment gates
💡
Customer impact quantification
Each incident correlated with affected sessions, transactions, and revenue — so you prioritise what matters to the business.
Business context on every alert
Business Impact

Connect technical failures to business risk.

Executives don't buy monitoring — they buy reduced business risk. Applicare traces every incident from the failing pod all the way to revenue.

Pod Failure
API Service Degraded
Checkout Requests Delayed
Payment Failures Increase
Customer Experience Impacted
Customer Outcomes

Proven results across industries.

Global Retail Enterprise
Challenge: Frequent pod restarts during flash sales causing checkout failures and lost revenue.
faster incident resolution
99.99%
uptime achieved
65%
fewer customer complaints
Financial Services Platform
Challenge: Cross-cluster service latency creating Sev-1 incidents and unpredictable infrastructure spend.
82%
lower MTTR
41%
infrastructure savings
96%
fewer Sev-1 incidents
Ecosystem Coverage

Built for the entire Red Hat OpenShift ecosystem.

Every OpenShift cluster — on-prem or cloud — with the Red Hat-native tools your platform team already runs.

🎩
OpenShift
🧩
Operators
🛒
OperatorHub
🐙
GitOps (Argo CD)
🔧
Pipelines (Tekton)
Service Mesh
📦
Quay Registry
Helm
🔥
Prometheus
🐧
RHEL CoreOS
Competitive Comparison

How Applicare stacks up.

Native tools give you signals. Dynatrace adds visibility. Applicare delivers causality, business impact, and remediation.

CapabilityTraditional MonitoringDynatraceApplicare
Metrics & Logs
Dependency Mapping~ Partial✓ Real-time
Root Cause Analysis✓ <60s
Business Impact Analysis~ Limited✓ To revenue
Explain Why Incidents Happened~ Alerts✓ ArcIn AI
Recommended Remediation Actions~ Limited✓ IntelliTune
Quick Start

Start understanding your clusters in minutes.

01
Connect your cluster
One OpenShift Operator (or oc) — on any OpenShift 4.x cluster, on-prem or in the cloud. No per-node agents to babysit.
02
Auto-discover dependencies
The entity graph maps every project, DeploymentConfig, pod, route, service, and database in under 10 seconds.
03
Investigate incidents
ArcIn correlates signals into plain-English root cause with customer impact — from your first deploy.
04
Optimize performance
IntelliTune recommends rollbacks, right-sizing, and fixes — closing the loop on reliability and cost.
Average time to first insight: under 30 minutes
Red Hat OpenShift Incident Intelligence

OpenShift monitoring shows you signals.
Applicare shows you causality.

What changed, why it failed, which services are affected, how customers are impacted, and what to do next — in under 60 seconds.