AI Brain · Core Intelligence Layer

Stop Reacting.
Start Predicting.

SupInsight uses Bayesian multi-variable causal inference to calculate RTO breach probability with a 95% confidence interval — hours before any incident occurs. Triggers automated remediation within 300ms.

RTO Breach Probability
12.6%
95% Confidence Interval · Next 60 minutes
IOPS Health
92%
Network Latency
78%
Replication Lag
95%
Storage Capacity
87%
Actionable policy generated · 300ms response
87.4%
Prediction Accuracy
95%
Confidence Interval
300ms
Auto-Remediation Speed
RLHF
Self-Evolution Method

Core Capabilities

Real-Time Health Portrait

Multi-dimensional monitoring data streams in real-time, forming a holistic health score for every business system. Correlates infrastructure state, network latency, storage IO, and business availability.

RTO Breach Prediction

Instead of alerting after disaster strikes, SupInsight calculates "the probability of RTO breach in the next 60 minutes" and triggers automatic optimization before any SLA violation.

RLHF Self-Evolution

Continuous training on PB-scale historical drill data. Incorporates operator feedback via Reinforcement Learning from Human Feedback. Dynamic model updates based on real workload patterns.

Bayesian Inference Architecture

P(RTO > T | E) = [P(E|R) · P(R)] / P(E)

Layer 1
Evidence Observation
  • IOPS / Latency
  • Packet Loss / Jitter
  • CPU / Memory / Queue
Layer 2
Causal Inference Engine
  • Bayesian Network PGM
  • P(RTO>T|E) = [P(E|R)·P(R)] / P(E)
  • Historical Priors + RLHF
Layer 3
Decision & Optimization
  • Trigger Pre-emptive Snapshot
  • Reroute Replication Path
  • Throttle Non-Critical IO

See SupInsight Predict Your Next Incident

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