I INFERENCING Inference Anomaly Radar
LIVE SYNTHETIC TELEMETRY

See the inference stack
break before users do.

Learn a normal serving baseline, stream synthetic telemetry, inject incidents, and detect abnormal latency, memory, queueing and throughput behavior in real time.

BASELINE→STREAM→DEVIATION→DETECT→TRIAGE
ANOMALY SCORE
8 /100

Normal behavior

Baseline Learning 12 / 24
ACTIVE INCIDENT
✓
No active incident Telemetry within learned range

04 · LIVE TELEMETRY

Inference health stream

Sample 0
Queue TTFT Decode Memory KV Errors
05 · SIGNAL DEVIATION

Current pressure vs. baseline

Standardized deviation
DETECTION LATENCY —

DRIFT SCORE 4

AFFECTED SIGNALS 0

CONFIDENCE 62%

ROOT-CAUSE HYPOTHESIS

FIRST RESPONSE

06 · INCIDENT TIMELINE

Detected anomalies

07 · INCIDENT RECORD

Machine-readable JSON


        


        

          
            
              How Inference Anomaly Radar works
              Rolling baseline + multivariate synthetic deviation
            
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              The radar learns a rolling baseline from recent synthetic telemetry. Each new sample
              is standardized against the baseline. Cross-signal deviation, sustained drift,
              and incident-specific relationships raise the combined anomaly score.
            

            

              The app does not monitor a real server. It is designed to demonstrate how live
              inference observability, baselines, anomaly detection, triage and root-cause
              reasoning can fit together in one control surface.
            

          

        

      

    


    
  


  
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