AI-Powered Intelligence

AI-Powered Observability
Intelligence

CoreLens AI transforms raw telemetry into instant understanding — correlating millions of events per second to surface root causes, predict failures, and recommend precise fixes before your users are ever affected.

Incident Detection

Incidents Detected Before Your Users Notice

Continuous AI monitoring across every signal in your stack — no thresholds to configure, no false alarms to chase.

01
Ingest at Scale
CoreLens ingests your logs, metrics, and distributed traces in real time at 2.4 million events per second with sub-millisecond latency.
02
Learn Your Baseline
AI baseline models observe your system's normal behavioral patterns across a 7-day rolling window, accounting for time-of-day and weekly seasonality.
03
Detect Deviations Instantly
Any deviation from baseline — even subtle latency creep or unusual error clustering — triggers detection within seconds.
04
Enrich with Context
Each alert is automatically enriched with severity classification, affected services, correlated traces, and a confidence score.
05
Notify the Right People
Deliver actionable alerts via Slack, PagerDuty, Teams, or Email — with full context, not just a raw metric value.
INCIDENT DETECTED
INC-4821

Database Connection Pool Exhausted

CRITICAL Detected 2 min ago
Affected Services
payment-servicecheckout-api
AI Confidence97%
Root cause: PostgreSQL max_connections (100) reached at 14:22:48 UTC. A long-running migration query on db-primary held 94 connections for 3m 12s, causing payment-service to queue and time out.
Root Cause Analysis

From 2 Hours of Debugging to 30 Seconds

AI correlates logs, traces, and metrics across your entire distributed system to surface the exact root cause — not just the symptom.

Before CoreLens
  • Manually scan thousands of log lines
  • Correlate timestamps across 5+ dashboards
  • Guess at root cause, apply a fix, hope for the best
  • Wake the database team at 3 AM
  • Average MTTR: 2 hours 14 minutes
With CoreLens AI
  • AI analyses all signals automatically within seconds
  • Root cause chain displayed in plain English
  • Specific, actionable fix recommendation provided
  • One-click runbook creation for future recurrence
  • Average MTTR: 28 seconds
CoreLens AI — Root Cause Analysis
Analyzing
U
Why did our payment service spike to 8000ms latency at 14:23 UTC?

Root Cause Identified ✓

Analyzed 847,219 log lines, 23 distributed traces, and 4 metric streams across your stack:

1.DB connection pool exhausted at 14:22:48 on db-primary — max_connections (100) reached
2.payment-service triggered a retry storm: 12,847 retries in 90 seconds, amplifying load 128×
3.Root trigger: migration job add_index_payments_v3 started at 14:20:11, holding 94 connections

Recommended Fix:

Set max_connections to 200, add PgBouncer in transaction mode, and enforce migration scheduling to off-peak windows (02:00–05:00 UTC).

Apply FixView Full TraceCreate Runbook
Log Intelligence

Thousands of Logs. One Clear Summary.

Stop drowning in raw log data. CoreLens AI reads every line and delivers human-readable intelligence — pattern clusters, anomaly highlights, and trend summaries — in seconds.

  • Pattern Detection
    Automatically cluster recurring error patterns and surface the most impactful ones.
  • Anomaly Clustering
    Group statistically unusual events together for efficient triage — no alert fatigue.
  • Plain-English Summaries
    AI generates a 2–3 sentence executive summary for any time window you select.
  • Trend Identification
    Detect slow-burn degradations that rule-based alerts would miss entirely.
  • Noise Reduction
    Filter out repetitive, low-value log noise before it reaches your on-call engineer.
AI Log Summary
Last 15 minutes
847,219
Total events
14 anomalies 98.4% normal
Traffic is within expected baseline with a slight uptick in payment-service latency (+12ms above P99 average). Two authentication services logged elevated 401 rates at 14:18–14:21 UTC — consistent with a scheduled penetration test. No production impact detected.
Key Patterns Found
API gateway handling 2.1M req/min — 8% above 7-day average
Repeated 'connection pool warn' cluster in db-replica-02 (×847)
Background job queue fully drained — no backlog detected
k8s pod restarts: 3 in 15 min (all OOMKilled — memory limit 512Mi)
Recommendations

Actionable Insights, Not Just Data

CoreLens AI doesn't just surface problems — it tells you exactly what to do next, with confidence-scored recommendations you can act on in one click.

Infrastructure

Scale Up Recommendation

CPU utilization on api-gateway has been above 78% for 22 consecutive minutes. AI recommends adding 2 worker nodes to the autoscaling group to restore headroom before the PM peak traffic window.

AI Confidence94%
Performance

Query Optimization

Slow query detected: SELECT * FROM orders WHERE customer_id = ? AND status = 'pending' — missing composite index on (customer_id, status). Estimated 94% reduction in query time after fix.

AI Confidence98%
Alert Quality

Alert Threshold Tuning

Your memory-usage alert fires 18 times per day due to a threshold set at 70%. AI analysis shows 85% is the true anomaly boundary for this service. Adjusting will reduce noise by 73%.

AI Confidence91%
Health Scoring

Your Infrastructure's Overall Health at a Glance

AI-computed health scores across every layer of your stack — updated every 60 seconds with explanations you can understand.

94
out of 100
Infrastructure Healthy
2 services require attention · 0 critical issues
Healthy (≥ 90)
Warning (70–89)
Critical (< 70)
API Gateway98%
Database Cluster87%
Payment Service92%
Worker Pool96%
Network Fabric99%
AI Chat

Ask Anything About Your Infrastructure

Natural language access to every signal in your stack. No query language to learn — just ask, and get instant, actionable answers.

CoreLens AI Chat
Online
Show me services with the highest error rate in the last hour

Top services by error rate (last 60 min):

ServiceError RateTrend
checkout-api4.72%
auth-service1.38%
inventory-svc0.94%
What's causing errors in checkout-api?

3 contributing factors identified:

1.HTTP 503 storm from downstream payment-gateway — 1,241 failures since 13:58 UTC, likely rate-limit enforcement
2.DB timeout pattern: SELECT * FROM orders WHERE … missing index on user_id, causing 2.4s scans
3.Memory pressure at 89% on checkout-api-pod-3 triggering GC pauses of 400–900ms — consider bumping memory limit to 1Gi
Ask about your infrastructure…
Predictive Alerts

Know About Problems 30 Minutes Before They Happen

CoreLens AI uses time-series forecasting models trained on your historical data to project where key metrics are heading — and alert you before they breach critical thresholds.

  • Trend Analysis
    Statistical trend decomposition identifies gradual degradations invisible to point-in-time thresholds.
  • ML Forecasting
    LSTM-based time-series models learn your system's hourly, daily, and weekly patterns to produce accurate predictions.
  • Confidence Thresholds
    Alerts only fire when the predictive model exceeds 85% confidence, eliminating speculative noise.
  • Configurable Lead Time
    Set alert lead times from 5 to 60 minutes depending on how long your remediation actions take.
Connection Pool UtilizationLive Forecast
PREDICTEDALERT ZONE94%NOWCurrent: 67%Predicted: 94%in 28 min-60m-50m-40m-30m-15mNow+15m+30m
Historical data
Predicted trajectory
Alert zone
Anomaly Detection

Your System's Normal, Redefined Every Hour

Adaptive baseline models that evolve with your system — no static thresholds, no manual threshold configuration required, ever.

Traffic Spike

Detects sudden request volume anomalies that deviate more than 3σ from rolling baseline, distinguishing real spikes from expected load patterns.

Detected 12 incidents this week

Error Rate Surge

Monitors error-to-request ratios per service endpoint, flagging sustained elevation even when absolute counts look normal due to traffic drop.

Detected 7 incidents this week

Latency Drift

Tracks P50, P95, and P99 latency independently. Detects gradual drift that builds over hours — the slowest and most dangerous type of degradation.

Detected 9 incidents this week

Resource Exhaustion

Monitors CPU, memory, disk I/O, and network bandwidth for depletion trajectories — catching saturation before your services start dropping requests.

Detected 4 incidents this week
No manual threshold configuration needed — ever

From Raw Data to Instant Understanding

Three phases. One seamless pipeline. Zero manual configuration.

01

COLLECT

Logs, metrics, distributed traces, and events are ingested from every service, host, and cloud provider simultaneously.

2.4M events/sec
02

ANALYZE

AI models correlate signals across all telemetry types simultaneously, building a unified causal graph of your system behavior.

10+ AI models running
03

ACT

Alerts, summaries, root cause analyses, and one-click recommendations are delivered the moment something demands your attention.

< 3s to insight
AI-Powered NOC

Your AI-Powered NOC Engineer, 24/7

CoreLens AI works around the clock so your team doesn't have to. Incident detection, root cause analysis, and actionable recommendations — delivered in seconds, any time of day.

No manual threshold tuning
Works in minutes
SOC 2 Type II certified