Smart incident detection
Predict root causes fast with automated event correlation powered by AI.
The real challenge
Too many alerts. Too little clarity
Why most RCA efforts fail:
- 01
Siloed logs, metrics, traces
- 02
No contextual linkage between events
- 03
Repetitive false positives
- 04
High MTTR due to guesswork
- 05
Human bias in identifying fault sources
Our approach
Patterns > volume
Calsoft’s ML-powered RCA framework includes:
Cross-layer data stitching (infra, app, user behavior, network)
Probabilistic event dependency mapping
Temporal pattern analysis (pre-failure trends, cascading faults)
Event-to-impact graph construction
NLP models for log summarization
Root cause scoring (severity, scope, frequency, novelty)
What we correlate
From edge to core
Events correlated across:
Compute: CPU, memory spikes, container lifecycle
Storage: IOPS drop, queue saturation
Network: latency, dropped packets, interface resets
Application: 500 errors, long GC, thread pool stalls
User behavior: login failures, API overload
External dependencies: 3rd-party API slowdown
Tangible outcomes
Fewer alerts. Faster recovery
Here’s how we get started
KPI
Before
After
Alerts per incident
20–100
1–3
MTTR
2–6 hrs
<15 min
False positives
Frequent
↓ by 80%
Manual investigations
Daily
Weekly
Escalation workload
High
↓ by 60%
How to start
Smarter RCA in 4 steps
Connect observability sources
Integrate logs, metrics, and traces into a unified pipeline.
Ingest event history
Use historical incidents and alerts to train correlation models
Activate event graph AI
Build time-based dependency maps and causality graphs.
Deploy and monitor accuracy
Track RCA confidence scores and retrain models as needed.
